Papers with Code

4.60

RATING

1140 M+

EXPOSURE

About this tool

Name

Papers with Code

Category

tools

Papers with Code is an open-access platform that connects machine learning research papers with their corresponding code implementations. It serves as a comprehensive resource for researchers, practitioners, and enthusiasts to access the latest developments in machine learning, facilitating reproducibility and collaboration within the community. Key Features: Extensive Repository: Hosts a vast collection of machine learning papers accompanied by code, covering a wide range of topics and applications. State-of-the-Art Benchmarks: Provides leaderboards and benchmarks for various machine learning tasks, enabling users to compare model performances and track progress in the field. Task and Dataset Catalogs: Offers organized lists of tasks and datasets, assisting users in finding relevant resources for their research or projects. Community Contributions: Encourages community involvement by allowing users to submit papers, code, and evaluations, fostering a collaborative environment.

Ratings and Reviews

4.80

Papers with Code is an invaluable resource for staying updated on the latest machine learning research. The combination of research papers and corresponding code implementations makes it easier to understand and apply new concepts. It's a must-visit site for anyone in the AI field.

March 15, 2024

Emily Johnson

4.90

I regularly use Papers with Code to find state-of-the-art models and their implementations. The platform's organization and the availability of benchmarks help me compare different approaches effectively. It's an essential tool for researchers and practitioners alike.

June 18, 2024

James Carter

4.60

Papers with Code bridges the gap between academic research and practical application. Having access to both papers and their code repositories accelerates my learning and development process. I highly recommend it to anyone looking to deepen their understanding of machine learning.

September 8, 2024

Sophia Martinez

How to use

Explore Research Papers: Visit the Papers with Code website to browse and search for machine learning papers of interest. Access Code Implementations: For each paper, find links to code repositories, enabling you to review, replicate, or build upon existing work. Utilize Benchmarks: Examine leaderboards to understand the current state-of-the-art models for specific tasks and assess their performance metrics. Discover Datasets: Navigate through the dataset catalog to find data suitable for your research or application needs. Contribute to the Community: If you have developed code for a paper or improved upon existing implementations, consider submitting your work to share with others.

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