Beginner

Introduction to Papers With Code

Papers With Code is the definitive platform for finding AI and machine learning research papers alongside their open-source implementations. It bridges the gap between academic research and practical application.

What Is Papers With Code?

Papers With Code (paperswithcode.com) is a free, open resource that automatically links machine learning papers to their GitHub repositories, datasets, and evaluation benchmarks. Created by the community and now maintained by Meta AI, it has become the go-to platform for:

  • Finding implementations: Locate working code for any AI research paper
  • Tracking state-of-the-art: See which methods achieve the best results on standard benchmarks
  • Discovering datasets: Browse thousands of curated datasets across every ML domain
  • Understanding methods: Explore a taxonomy of ML methods, architectures, and components
  • Staying current: Follow trending papers and new breakthroughs as they happen
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Why it matters: Before Papers With Code, finding the actual implementation of a research paper was a frustrating treasure hunt. Now, over 150,000 papers are linked to code, making AI research accessible and reproducible.

Key Features at a Glance

FeatureDescriptionUse Case
Papers150,000+ papers with linked code repositoriesFind implementations of specific techniques
SOTA Leaderboards5,000+ benchmarks with performance rankingsCompare model performance objectively
Datasets7,000+ curated datasets with metadataFind training and evaluation data
MethodsTaxonomy of ML components and architecturesUnderstand how techniques relate to each other
TrendingDaily updated trending papers and repositoriesStay on top of new research

How Papers With Code Works

The platform combines automated extraction with community contributions to build a comprehensive knowledge graph of ML research:

  1. Paper Ingestion

    New papers from arXiv and top conferences (NeurIPS, ICML, ICLR, CVPR, ACL, etc.) are automatically indexed and parsed.

  2. Code Linking

    GitHub repositories are matched to papers through automated detection and community submissions. Each paper may have multiple implementations.

  3. Benchmark Tracking

    Results reported in papers are extracted and added to leaderboards, allowing direct comparison across methods on standard benchmarks.

  4. Community Curation

    Users can add missing links, correct errors, and contribute new benchmarks, datasets, and method descriptions.

Who Should Use Papers With Code?

  • Researchers: Track related work, compare baselines, and find code to build upon
  • ML Engineers: Find proven implementations to adapt for production use cases
  • Students: Learn by studying real implementations alongside the papers that describe them
  • Product Teams: Evaluate which approaches achieve the best results for a given task
  • Anyone curious about AI: Explore what is happening at the frontier of machine learning research
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Getting started: Head to paperswithcode.com and try searching for a topic you are interested in. In the next lesson, we will cover advanced navigation and search strategies.

What You'll Learn in This Course

This course will teach you to use Papers With Code effectively as part of your AI research and development workflow:

  • How to navigate the site and find papers with code efficiently
  • Understanding and using SOTA benchmarks and leaderboards
  • Exploring and selecting datasets for your projects
  • Using the methods taxonomy to understand ML architectures
  • Best practices for reproducing results and contributing to the platform