Papers with Code

Papers with Code is a free and open-access platform that provides a comprehensive collection of machine learning papers, code implementations, datasets, methods, and evaluation tables. It serves as a bridge between research and practical implementation, making state-of-the-art machine learning resources easily accessible to the community.

Key features:
- Extensive repository of machine learning papers, code implementations, datasets, methods, and evaluation tables
- Open contribution system allowing anyone to add new code implementations, evaluation tables, or tasks
- Edit buttons on paper and task pages for easy community contributions
- Monitoring of all edits on an open Slack channel to ensure data quality
- Specialized portals for papers with code in various fields such as astronomy, physics, computer sciences, mathematics, and statistics
- Open licensing under CC BY-SA, encouraging free use and distribution
- Integration of data from other resources like NLP-progress, EFF AI metrics, SQuAD, and RedditSota

How it works:
Users can search for and browse papers, code, datasets, and methods on the web platform. They can contribute by adding new code implementations or updating evaluation tables using the edit buttons available on the site. Users can also download data, including papers with abstracts, links between papers and code, evaluation tables, methods, and datasets.

Integrations:
Papers with Code integrates with various data sources and platforms, including NLP-progress, EFF AI metrics, SQuAD, and RedditSota.

Use of AI:
Papers with Code leverages generative AI, particularly Retrieval Augmented Generation (RAG) techniques, to enhance the retrieval and generation of relevant information from its extensive database.

How to access:
Papers with Code is available as a web application, making it accessible to users worldwide. It is not available as a mobile app, API, or SDK.

The platform is ideal for researchers, developers, academics, and industry professionals who are looking for the latest papers, code implementations, state-of-the-art methods, and datasets for their machine learning projects or research. The core team behind Papers with Code is based in Meta AI Research.