Papers with Code

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

Key features:
- Extensive Repository: Access to a vast collection of machine learning papers, code implementations, datasets, methods, and evaluation tables.
- Community Contributions: Open system allowing users to add new code implementations, evaluation tables, or tasks, with easy-to-use edit buttons on paper and task pages.
- Specialized Portals: Dedicated sections for papers with code in fields such as astronomy, physics, computer sciences, mathematics, and statistics.
- Open Licensing: All content is openly licensed under CC BY-SA, promoting free use and distribution.
- Additional Data Sources: Incorporates data from other resources like NLP-progress, EFF AI metrics, SQuAD, and RedditSota.

How it works:
1. Users search or browse for specific papers, code, datasets, or methods on the web platform.
2. The platform provides state-of-the-art results and benchmarks for various machine learning tasks.
3. Users can contribute by adding new code implementations or updating evaluation tables using edit buttons on the site.
4. Data, including papers with abstracts, links between papers and code, evaluation tables, methods, and datasets, can be downloaded by users.

Integrations:
NLP-progress, EFF AI metrics, SQuAD, RedditSota

Use of AI:
Papers with Code utilizes generative AI, particularly in Retrieval Augmented Generation (RAG) techniques. This involves using large language models to enhance the retrieval and generation of relevant information from its extensive database.

Target users:
- Researchers seeking the latest papers and code implementations
- Developers looking for state-of-the-art methods and datasets for machine learning projects
- Academics interested in contributing to and staying updated with the latest research
- Industry professionals incorporating advanced machine learning techniques into their workflows

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

Company information:
The core team behind Papers with Code is based in Meta AI Research. The platform is a community-driven project, ensuring that all data and contributions are openly licensed and freely accessible.