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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'decision'})
This happened while the csv dataset builder was generating data using
hf://datasets/CIKM-23/MetaRev/conference_papers.csv (at revision e57202aa741f41e39de67134e3d186861f4247d7)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
paper_id: int64
abstract: string
title: string
conference: string
forum_id: string
peer_reviews: string
meta_review: string
author_reply: string
level_4_1_replies: string
level_4_2_replies: string
level_4_3_replies: string
decision: string
keywords: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1842
to
{'paper_id': Value(dtype='int64', id=None), 'abstract': Value(dtype='string', id=None), 'title': Value(dtype='string', id=None), 'conference': Value(dtype='string', id=None), 'forum_id': Value(dtype='string', id=None), 'peer_reviews': Value(dtype='string', id=None), 'meta_review': Value(dtype='string', id=None), 'keywords': Value(dtype='string', id=None), 'author_reply': Value(dtype='string', id=None), 'level_4_1_replies': Value(dtype='string', id=None), 'level_4_2_replies': Value(dtype='string', id=None), 'level_4_3_replies': Value(dtype='string', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 1 new columns ({'decision'})
This happened while the csv dataset builder was generating data using
hf://datasets/CIKM-23/MetaRev/conference_papers.csv (at revision e57202aa741f41e39de67134e3d186861f4247d7)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
paper_id int64 | abstract string | title string | conference string | forum_id string | peer_reviews string | meta_review string | keywords string | author_reply string | level_4_1_replies string | level_4_2_replies string | level_4_3_replies string |
|---|---|---|---|---|---|---|---|---|---|---|---|
682 | In cities with tall buildings, emergency responders need an accurate floor level location to find 911 callers quickly. We introduce a system to estimate a victim's floor level via their mobile device's sensor data in a two-step process. First, we train a neural network to determine when a smartphone enters or exits a b... | Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data | ICLR.cc/2018/Conference | ryBnUWb0b | [{'ddate': None, 'original': None, 'tddate': 1511728326898, 'tmdate': 1515642491552, 'tcdate': 1511728310814, 'number': 1, 'cdate': 1511728310814, 'id': 'B11TNj_gM', 'invitation': 'ICLR.cc/2018/Conference/-/Paper682/Official_Review', 'forum': 'ryBnUWb0b', 'replyto': 'ryBnUWb0b', 'signatures': ['ICLR.cc/2018/Conference/... | Reviewers agree that the paper is well done and addresses an interesting problem, but uses fairly standard ML techniques.
The authors have responded to rebuttals with careful revisions, and improved results. | ['Recurrent Neural Networks', 'RNN', 'LSTM', 'Mobile Device', 'Sensors'] | [{'tddate': None, 'ddate': None, 'tmdate': 1515129408153, 'tcdate': 1515128426667, 'number': 8, 'cdate': 1515128426667, 'id': 'S1mOIY2mf', 'invitation': 'ICLR.cc/2018/Conference/-/Paper682/Official_Comment', 'forum': 'ryBnUWb0b', 'replyto': 'ryBnUWb0b', 'signatures': ['ICLR.cc/2018/Conference/Paper682/Authors'], 'reade... | [{'tddate': None, 'ddate': None, 'tmdate': 1515128678906, 'tcdate': 1515128678906, 'number': 10, 'cdate': 1515128678906, 'id': 'B1kuDK27M', 'invitation': 'ICLR.cc/2018/Conference/-/Paper682/Official_Comment', 'forum': 'ryBnUWb0b', 'replyto': 'B11TNj_gM', 'signatures': ['ICLR.cc/2018/Conference/Paper682/Authors'], 'read... | [] | [] |
41 | We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and ERL2. Results are presented on a novel environment we call 'Krazy World' and a set of maze environments. We show E-MAML and ERL2 deliver better performance on tasks where expl... | Some Considerations on Learning to Explore via Meta-Reinforcement Learning | ICLR.cc/2018/Conference | Skk3Jm96W | [{'tddate': None, 'ddate': None, 'original': None, 'tmdate': 1515642445131, 'tcdate': 1511540774475, 'number': 1, 'cdate': 1511540774475, 'id': 'SJ0Q_6Hlf', 'invitation': 'ICLR.cc/2018/Conference/-/Paper41/Official_Review', 'forum': 'Skk3Jm96W', 'replyto': 'Skk3Jm96W', 'signatures': ['ICLR.cc/2018/Conference/Paper41/An... | Overall, the paper is missing a couple of ingredients that would put it over the bar for acceptance:
- I am mystified by statements such as "RL2 no longer gets the best final performance." from one revision to another, as I have lower confidence in the results now.
- More importantly, the paper is missing comparisons... | ['reinforcement learning', 'rl', 'exploration', 'meta learning', 'meta reinforcement learning', 'curiosity'] | [{'tddate': None, 'ddate': None, 'tmdate': 1514120561182, 'tcdate': 1514120561182, 'number': 2, 'cdate': 1514120561182, 'id': 'rkYuBQTfG', 'invitation': 'ICLR.cc/2018/Conference/-/Paper41/Official_Comment', 'forum': 'Skk3Jm96W', 'replyto': 'Skk3Jm96W', 'signatures': ['ICLR.cc/2018/Conference/Paper41/Authors'], 'readers... | [{'tddate': None, 'ddate': None, 'tmdate': 1514120938026, 'tcdate': 1514120938026, 'number': 5, 'cdate': 1514120938026, 'id': 'rJfePXpMG', 'invitation': 'ICLR.cc/2018/Conference/-/Paper41/Official_Comment', 'forum': 'Skk3Jm96W', 'replyto': 'SJ0Q_6Hlf', 'signatures': ['ICLR.cc/2018/Conference/Paper41/Authors'], 'readers... | [{'tddate': None, 'ddate': None, 'tmdate': 1514913787656, 'tcdate': 1514913787656, 'number': 10, 'cdate': 1514913787656, 'id': 'H1NWlrYmM', 'invitation': 'ICLR.cc/2018/Conference/-/Paper41/Official_Comment', 'forum': 'Skk3Jm96W', 'replyto': 'rJfePXpMG', 'signatures': ['ICLR.cc/2018/Conference/Paper41/AnonReviewer3'], '... | [] |
455 | We present Merged-Averaged Classifiers via Hashing (MACH) for $K$-classification with large $K$. Compared to traditional one-vs-all classifiers that require $O(Kd)$ memory and inference cost, MACH only need $O(d\\log{K})$ memory while only requiring $O(K\\log{K} + d\\log{K})$ operation for inference. MACH is the first ... | MACH: Embarrassingly parallel $K$-class classification in $O(d\\log{K})$ memory and $O(K\\log{K} + d\\log{K})$ time, instead of $O(Kd)$ | ICLR.cc/2018/Conference | r1RQdCg0W | [{'tddate': None, 'ddate': None, 'original': None, 'tmdate': 1515642451218, 'tcdate': 1511810907824, 'number': 3, 'cdate': 1511810907824, 'id': 'H1VwD15lG', 'invitation': 'ICLR.cc/2018/Conference/-/Paper455/Official_Review', 'forum': 'r1RQdCg0W', 'replyto': 'r1RQdCg0W', 'signatures': ['ICLR.cc/2018/Conference/Paper455/... | There is a very nice discussion with one of the reviewers on the experiments, that I think would need to be battened down in an ideal setting. I'm also a bit surprised at the lack of discussion or comparison to two seemingly highly related papers:
1. T. G. Dietterich and G. Bakiri (1995) Solving Multiclass via Error C... | ['Extreme Classification', 'Large-scale learning', 'hashing', 'GPU', 'High Performance Computing'] | [] | [{'tddate': None, 'ddate': None, 'tmdate': 1514112380547, 'tcdate': 1514112380547, 'number': 2, 'cdate': 1514112380547, 'id': 'HJVtSZaMz', 'invitation': 'ICLR.cc/2018/Conference/-/Paper455/Official_Comment', 'forum': 'r1RQdCg0W', 'replyto': 'H1VwD15lG', 'signatures': ['ICLR.cc/2018/Conference/Paper455/Authors'], 'reade... | [{'tddate': None, 'ddate': None, 'tmdate': 1514309066267, 'tcdate': 1514309066267, 'number': 4, 'cdate': 1514309066267, 'id': 'SJfRHbeQz', 'invitation': 'ICLR.cc/2018/Conference/-/Paper455/Official_Comment', 'forum': 'r1RQdCg0W', 'replyto': 'Skch5gafG', 'signatures': ['ICLR.cc/2018/Conference/Paper455/AnonReviewer1'], ... | [{'tddate': None, 'ddate': None, 'tmdate': 1514315094725, 'tcdate': 1514314592603, 'number': 5, 'cdate': 1514314592603, 'id': 'S1YDsMlXf', 'invitation': 'ICLR.cc/2018/Conference/-/Paper455/Official_Comment', 'forum': 'r1RQdCg0W', 'replyto': 'SJfRHbeQz', 'signatures': ['ICLR.cc/2018/Conference/Paper455/Authors'], 'reade... |
184 | The goal of imitation learning (IL) is to enable a learner to imitate an expert’s behavior given the expert’s demonstrations. Recently, generative adversarial imitation learning (GAIL) has successfully achieved it even on complex continuous control tasks. However, GAIL requires a huge number of interactions with enviro... | Deterministic Policy Imitation Gradient Algorithm | ICLR.cc/2018/Conference | rJ3fy0k0Z | [{'tddate': None, 'ddate': None, 'original': None, 'tmdate': 1515642405594, 'tcdate': 1511726708162, 'number': 2, 'cdate': 1511726708162, 'id': 'B1nuCculG', 'invitation': 'ICLR.cc/2018/Conference/-/Paper184/Official_Review', 'forum': 'rJ3fy0k0Z', 'replyto': 'rJ3fy0k0Z', 'signatures': ['ICLR.cc/2018/Conference/Paper184/... | All of the reviewers found some aspects of the formulation and experiments interesting, but they found the paper hard to read and understand. Some of the components of the technique such as the state screening function (SSF) seem ad-hoc and heuristic without much justification. Please improve the exposition and remove ... | ['Imitation Learning'] | [] | [{'tddate': None, 'ddate': None, 'tmdate': 1515179172495, 'tcdate': 1515179172495, 'number': 2, 'cdate': 1515179172495, 'id': 'SknsnHTQG', 'invitation': 'ICLR.cc/2018/Conference/-/Paper184/Official_Comment', 'forum': 'rJ3fy0k0Z', 'replyto': 'B1nuCculG', 'signatures': ['ICLR.cc/2018/Conference/Paper184/Authors'], 'reade... | [] | [] |
503 | The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various hand-designed alternatives to ReLU have been proposed, none have managed to... | Searching for Activation Functions | ICLR.cc/2018/Conference | SkBYYyZRZ | [{'tddate': None, 'ddate': None, 'original': None, 'tmdate': 1515642457935, 'tcdate': 1511810827232, 'number': 2, 'cdate': 1511810827232, 'id': 'Hy7GD19gM', 'invitation': 'ICLR.cc/2018/Conference/-/Paper503/Official_Review', 'forum': 'SkBYYyZRZ', 'replyto': 'SkBYYyZRZ', 'signatures': ['ICLR.cc/2018/Conference/Paper503/... | The author's propose to use swish and show that it performs significantly better than Relus on sota vision models. Reviewers and anonymous ones counter that PRelus should be doing quite well too. Unfortunately, the paper falls in the category where it is hard to prove the utility of the method through one paper alone, ... | ['meta learning', 'activation functions'] | [{'tddate': None, 'ddate': None, 'tmdate': 1515211572929, 'tcdate': 1515211572929, 'number': 5, 'cdate': 1515211572929, 'id': 'r1a4oTTmz', 'invitation': 'ICLR.cc/2018/Conference/-/Paper503/Official_Comment', 'forum': 'SkBYYyZRZ', 'replyto': 'SkBYYyZRZ', 'signatures': ['ICLR.cc/2018/Conference/Paper503/Authors'], 'reade... | [{'tddate': None, 'ddate': None, 'tmdate': 1514910923909, 'tcdate': 1514775194822, 'number': 2, 'cdate': 1514775194822, 'id': 'rkQoM7wmM', 'invitation': 'ICLR.cc/2018/Conference/-/Paper503/Official_Comment', 'forum': 'SkBYYyZRZ', 'replyto': 'Hy7GD19gM', 'signatures': ['ICLR.cc/2018/Conference/Paper503/Authors'], 'reade... | [{'tddate': None, 'ddate': None, 'tmdate': 1515791902283, 'tcdate': 1515791902283, 'number': 7, 'cdate': 1515791902283, 'id': 'BkIXIiLNG', 'invitation': 'ICLR.cc/2018/Conference/-/Paper503/Official_Comment', 'forum': 'SkBYYyZRZ', 'replyto': 'rkQoM7wmM', 'signatures': ['ICLR.cc/2018/Conference/Paper503/AnonReviewer1'], ... | [{'tddate': None, 'ddate': None, 'tmdate': 1515211674177, 'tcdate': 1515211674177, 'number': 6, 'cdate': 1515211674177, 'id': 'Skfsiap7G', 'invitation': 'ICLR.cc/2018/Conference/-/Paper503/Official_Comment', 'forum': 'SkBYYyZRZ', 'replyto': 'rJMj2S57z', 'signatures': ['ICLR.cc/2018/Conference/Paper503/Authors'], 'reade... |
370 | We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks wh... | Improving Search Through A3C Reinforcement Learning Based Conversational Agent | ICLR.cc/2018/Conference | rkfbLilAb | [{'tddate': None, 'ddate': None, 'original': None, 'tmdate': 1515856439645, 'tcdate': 1511818885213, 'number': 3, 'cdate': 1511818885213, 'id': 'Hy4tIW5xf', 'invitation': 'ICLR.cc/2018/Conference/-/Paper370/Official_Review', 'forum': 'rkfbLilAb', 'replyto': 'rkfbLilAb', 'signatures': ['ICLR.cc/2018/Conference/Paper370/... | meta score: 4
This paper is primarily an application paper applying known RL techniques to dialogue. Very little reference to the extensive literature in this area.
Pros:
- interesting application (digital search)
- revised version contains subjective evaluation of experiments
Cons:
- limited technical novelty... | ['Subjective search', 'Reinforcement Learning', 'Conversational Agent', 'Virtual user model', 'A3C', 'Context aggregation'] | [{'tddate': None, 'ddate': None, 'tmdate': 1515160693860, 'tcdate': 1515160693860, 'number': 1, 'cdate': 1515160693860, 'id': 'HkAuVWpmz', 'invitation': 'ICLR.cc/2018/Conference/-/Paper370/Official_Comment', 'forum': 'rkfbLilAb', 'replyto': 'rkfbLilAb', 'signatures': ['ICLR.cc/2018/Conference/Paper370/Authors'], 'reade... | [{'tddate': None, 'ddate': None, 'tmdate': 1515172086452, 'tcdate': 1515163435116, 'number': 3, 'cdate': 1515163435116, 'id': 'H1QVkGpmM', 'invitation': 'ICLR.cc/2018/Conference/-/Paper370/Official_Comment', 'forum': 'rkfbLilAb', 'replyto': 'Hy4tIW5xf', 'signatures': ['ICLR.cc/2018/Conference/Paper370/Authors'], 'reade... | [] | [] |
390 | Identifying analogies across domains without supervision is a key task for artificial intelligence. Recent advances in cross domain image mapping have concentrated on translating images across domains. Although the progress made is impressive, the visual fidelity many times does not suffice for identifying the matching... | Identifying Analogies Across Domains | ICLR.cc/2018/Conference | BkN_r2lR- | [{'tddate': None, 'ddate': None, 'original': None, 'tmdate': 1515642442663, 'tcdate': 1512294804088, 'number': 3, 'cdate': 1512294804088, 'id': 'ryhcYB-bG', 'invitation': 'ICLR.cc/2018/Conference/-/Paper390/Official_Review', 'forum': 'BkN_r2lR-', 'replyto': 'BkN_r2lR-', 'signatures': ['ICLR.cc/2018/Conference/Paper390/... | This paper builds on top of Cycle GAN ideas where the main idea is to jointly optimize the domain-level translation function with an instance-level matching objective. Initially the paper received two negative reviews (4,5) and a positive (7). After the rebuttal and several back and forth between the first reviewer and... | ['unsupervised mapping', 'cross domain mapping'] | [{'tddate': None, 'ddate': None, 'tmdate': 1514987205323, 'tcdate': 1514987205323, 'number': 5, 'cdate': 1514987205323, 'id': 'rJ6aA85QG', 'invitation': 'ICLR.cc/2018/Conference/-/Paper390/Official_Comment', 'forum': 'BkN_r2lR-', 'replyto': 'BkN_r2lR-', 'signatures': ['ICLR.cc/2018/Conference/Paper390/Authors'], 'reade... | [{'tddate': None, 'ddate': None, 'tmdate': 1513188478089, 'tcdate': 1513188135968, 'number': 3, 'cdate': 1513188135968, 'id': 'rklEiy1Mz', 'invitation': 'ICLR.cc/2018/Conference/-/Paper390/Official_Comment', 'forum': 'BkN_r2lR-', 'replyto': 'ryhcYB-bG', 'signatures': ['ICLR.cc/2018/Conference/Paper390/Authors'], 'reade... | [{'tddate': None, 'ddate': None, 'tmdate': 1515816395809, 'tcdate': 1515816395809, 'number': 6, 'cdate': 1515816395809, 'id': 'ByECSWv4z', 'invitation': 'ICLR.cc/2018/Conference/-/Paper390/Official_Comment', 'forum': 'BkN_r2lR-', 'replyto': 'Byqw2JyGf', 'signatures': ['ICLR.cc/2018/Conference/Paper390/AnonReviewer2'], ... | [{'tddate': None, 'ddate': None, 'tmdate': 1516055638897, 'tcdate': 1516055638897, 'number': 7, 'cdate': 1516055638897, 'id': 'BkyDnj5VG', 'invitation': 'ICLR.cc/2018/Conference/-/Paper390/Official_Comment', 'forum': 'BkN_r2lR-', 'replyto': 'ByECSWv4z', 'signatures': ['ICLR.cc/2018/Conference/Paper390/Authors'], 'reade... |
366 | "Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (S(...TRUNCATED) | Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling | ICLR.cc/2018/Conference | H1cWzoxA- | "[{'ddate': None, 'original': None, 'tddate': 1511806221587, 'tmdate': 1515642439599, 'tcdate': 1511(...TRUNCATED) | "The proposed Bi-BloSAN is a two-levels' block SAN, which has both parallelization efficiency and me(...TRUNCATED) | "['deep learning', 'attention mechanism', 'sequence modeling', 'natural language processing', 'sente(...TRUNCATED) | "[{'tddate': None, 'ddate': None, 'tmdate': 1513062985913, 'tcdate': 1513062985913, 'number': 5, 'cd(...TRUNCATED) | "[{'tddate': None, 'ddate': None, 'tmdate': 1513062835663, 'tcdate': 1513062720964, 'number': 4, 'cd(...TRUNCATED) | [] | [] |
916 | "To train an inference network jointly with a deep generative topic model, making it both scalable t(...TRUNCATED) | WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling | ICLR.cc/2018/Conference | S1cZsf-RW | "[{'tddate': None, 'ddate': None, 'original': None, 'tmdate': 1515642530672, 'tcdate': 1511899037154(...TRUNCATED) | "The paper proposes a new approach for scalable training of deep topic models based on amortized inf(...TRUNCATED) | [] | [] | "[{'tddate': None, 'ddate': None, 'tmdate': 1513573461760, 'tcdate': 1513573461760, 'number': 2, 'cd(...TRUNCATED) | "[{'tddate': None, 'ddate': None, 'tmdate': 1520324393097, 'tcdate': 1520324393097, 'number': 1, 'cd(...TRUNCATED) | [] |
493 | "We analyze the expressiveness and loss surface of practical deep convolutional\nneural networks (CN(...TRUNCATED) | The loss surface and expressivity of deep convolutional neural networks | ICLR.cc/2018/Conference | BJjquybCW | "[{'tddate': None, 'ddate': None, 'original': None, 'tmdate': 1515642456435, 'tcdate': 1513195189900(...TRUNCATED) | "Dear authors,\n\nWhile I appreciate the result that a convolutional layer can have full rank output(...TRUNCATED) | "['convolutional neural networks', 'loss surface', 'expressivity', 'critical point', 'global minima'(...TRUNCATED) | [] | "[{'tddate': None, 'ddate': None, 'tmdate': 1515186899255, 'tcdate': 1515186899255, 'number': 4, 'cd(...TRUNCATED) | [] | [] |
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