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November 21, 2024
The results of the NeurIPS 2024 Datasets and Benchmarks Track are now accessible on Hugging Face Paper Central. This provides researchers and developers with comprehensive insights into the latest developments in the field of data-centric AI research.
From the 460 submitted contributions, Hugging Face hosts 138 models, 108 datasets, and 74 Spaces. In addition, Paper Central provides access to over 250 related GitHub repositories. This collection represents a valuable resource for the AI community, enabling easy exploration and utilization of the research results.
The NeurIPS Datasets and Benchmarks Track plays a crucial role in the advancement of machine learning. High-quality datasets and benchmarks are essential for the development and evaluation of new AI methods. The track offers a platform for the publication and discussion of innovative approaches to dataset development and curation.
A central criterion for the works accepted in the track is the accessibility of the datasets. The data should be findable and usable without personal requests to the authors, and any required code should be open source. The submissions are reviewed according to strict criteria specifically tailored to datasets and benchmarks. These include, among others, the documentation of data collection, the investigation of potential biases in the data, and ensuring long-term availability.
The contributions accepted in the Datasets and Benchmarks Track are part of the main conference and are published together with the other conference papers in the official NeurIPS Proceedings. This underscores the importance of the track for the entire machine learning community.
The track actively promotes the open-source movement by supporting the submission of open-source libraries and tools that accelerate AI research. This contributes to the transparency and reproducibility of research results and enables broader community participation.
By providing the NeurIPS 2024 Datasets and Benchmarks results on Hugging Face Paper Central, access to these important resources for the AI community is significantly simplified. The platform offers a central hub for the exploration, download, and utilization of the datasets, models, and related materials.
The publication of the NeurIPS 2024 Datasets and Benchmarks results on Hugging Face is an important step for data-centric AI research. The extensive collection of resources will drive the development and evaluation of new AI methods and contribute to the advancement of the entire field.
Bibliographie: https://twitter.com/IAMJBDEL/status/1859019150645174411 https://neurips.cc/Conferences/2024/CallForDatasetsBenchmarks https://neurips.cc/virtual/2024/events/datasets-benchmarks-2024 https://huggingface.co/datasets/huggingface/paper-central-data https://nips.cc/virtual/2024/papers.html https://openreview.net/group?id=NeurIPS.cc/2024/Datasets_and_Benchmarks_Track https://huggingface.co/docs/datasets/index https://huggingface.co/datasets/librarian-bot/dataset_abstracts/viewer