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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    IndexError
Message:      list index out of range
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1859, in _prepare_split_single
                  original_shard_lengths[original_shard_id] += len(table)
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
              IndexError: list index out of range
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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ICLR.cc/2023/Conference/Paper1762/-/Official_Comment
{ "title": "Response to Reviewer FkFJ (4/4)", "comment": "**Q9** Could give an intuition for why not freezing the general layers improves the acc? If there is general knowledge in those layers, why change it? How much can we modify those layers and still prevent forgetting?\n\n**A9** We thank the reviewer for the s...
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ICLR.cc/2019/Conference/-/Paper1226/Public_Comment
{ "comment": "I enjoyed reading your paper, your approach is quite novel in my opinion. One question: why is the dataset used for evaluation in your paper not those commonly used for testing super resolution algorithms? The commonly used datasets for image super-resolution are BSD100, Urban100 and DIV2K, as in SRGAN ...
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ICLR.cc/2022/Conference/Paper499/-/Official_Review
{ "summary_of_the_paper": "The paper provides an extensive study on 1) the difference between human annotations and synthetic ones and 2) the impact on the classifier learning from human annotations and synthetic ones. To this end, the paper crowdsources the human annotations for CIFAR10 and CIFAR100 from Amazon Mech...
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{ "title": "Response to Reviewer#1 bkFN", "comment": "Thanks for your nice summary and positive comments on our novelty and contributions! In the following, we give detailed response to your comments one by one.\n\n>*1. +/- Ablation study has been performed to analyze the benefit of the different modules. The scale...
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NeurIPS.cc/2021/Conference/Paper5290/-/Official_Comment
{ "title": "Response to Reviewer#4 vvmM", "comment": "Thanks for your nice summary and positive comments on our method and results! In the following, we give detailed response to your comments one by one.\n\n> *A proper motivation of the proposed methods is needed.\n a) The transformer-like formulation in (2) is pr...
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NeurIPS.cc/2021/Conference/Paper8643/-/Official_Comment
{ "title": "Response to Reviewer em8a", "comment": "We first would like to thank the reviewer for giving us valuable comments. \n\n1. **Q**: The attention module follows PREDATOR's transformer. The encoder module is the coarse step and the decoder module is the fine step in this submission. Based on my understandin...
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ICLR.cc/2019/Conference/-/Paper1107/Official_Comment
{ "title": "This paper will be presented at CVPR 2019", "comment": "This paper will appear at CVPR 2019. \n\nThe final version of the paper is available on arXiv: https://arxiv.org/abs/1810.01455\nWe have made code available on GitHub: https://github.com/piergiaj/representation-flow-cvpr19\n\nSome visualizations of...
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NeurIPS.cc/2021/Conference/Paper5290/-/Official_Comment
{ "title": "Response to Reviewer#3 2ePa", "comment": "Thanks for your nice summary of our work! In the following, we give detailed response to your comments one by one.\n\n>*The method is described well. The experiments show that the method can perform well in terms of quantitative analysis and qualitative comparis...
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ICLR.cc/2020/Conference/Paper208/-/Official_Review
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ICLR.cc/2022/Conference/Paper1780/-/Official_Review
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ICLR.cc/2022/Conference/Paper1480/-/Official_Comment
{ "title": "Additional experimental results", "comment": "As your suggested, we have added performance comparison with two extra papers that you mentioned: EGNN and DPGN methods. As shown in Table 1, our method is able to outperform the DPGN (CVPR20) by 10.78% on 1-shot learning, and outperform the EGNN (CVPR19) by...
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ICLR.cc/2023/Conference/Paper578/-/Official_Comment
{ "title": "Response to Reviewer Cq4q (Part 1/3)", "comment": "Thanks for your precious time reading this paper. We are delighted that you found the paper technically sound and easy to follow. And your constructive suggestions are of great help to us.\n\nPlease refer to the detailed response below, regarding the co...
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ICLR.cc/2022/Conference/Paper1230/-/Official_Review
{ "summary_of_the_paper": "The paper proposes an OOD setting emphasizing on texture and semantics. The authors propose an OOD detection method which disentangles texture and semantics. The method achieves SoA performances.", "main_review": "Strengths:\n- The separation of texture and semantics is reasonable\n- SoA ...
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ICLR.cc/2022/Conference/Paper1480/-/Official_Review
{ "summary_of_the_paper": "[1] This paper follows the line of metric-based few-shot learning methods with GNNs. The novelty of this paper is to design dual GNN graphs to capture the consistency of label prediction from the support set to the query set and its reversible task (from the query set to the support set).\n...
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{ "venue": "CVPR Workshops 2018", "venueid": "dblp.org/conf/CVPR/2018", "_bibtex": "@inproceedings{DBLP:conf/cvpr/KushwahaS0VRC18,\n author={Vineet Kushwaha and Maneet Singh and Richa Singh and Mayank Vatsa and Nalini K. Ratha and Rama Chellappa},\n title={Disguised Faces in the Wild},\n year={2018},\n cdate=...
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{ "title": "", "review": "Another minor comment related to the visualization method: since there is no iterative 'inference' step typical of deconv. nets (the features are already given by a direct forward pass) then this method is perhaps more similar to this old paper of mine:\r\nM. Ranzato, F.J. Huang, Y. Bourea...
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{ "title": "Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "decision": "conferenceOral-iclr2013-conference", "abstract": "We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operati...
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{ "venue": "CVPR Workshops 2004", "venueid": "dblp.org/conf/CVPR/2004", "_bibtex": "@inproceedings{DBLP:conf/cvpr/HoogsM04,\n author={Anthony Hoogs and Gérard G. Medioni},\n title={An Overview of the 2004 Workshop on Perceptual Organization in Computer Vision},\n year={2004},\n cdate={1072915200000},\n pages...
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{ "venue": "CVPR2007", "venueid": "dblp.org/conf/CVPR/2007", "_bibtex": "@inproceedings{DBLP:conf/cvpr/BuchananF07,\n author={Aeron Buchanan and Andrew W. Fitzgibbon},\n title={Combining local and global motion models for feature point tracking.},\n year={2007},\n cdate={1167609600000},\n url={https://doi.or...
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End of preview.

introducing open-review, we have rewritten internal _api calls and collected metadata for all the major conferences i.e. ICLR, ICML, CVPR. we are releasing this dataset on HF and willing to work close to open-rewview team to abuse of their API.

why/we made this dataset

we wanted to have a personal database for open-review papers for point-of-interest conf, and we wanted to stress test how far, open-review have reached security after last year breach.

will we share code

no, we don't share code that can be used to exploit bugs, we respect community run projects and willing to work with teams to make internet a better place.

how to reach

email: research@sleeping-ai.com and it is being published under Project Uncertainty.

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