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The ml-surveys repository is a broad, maintainable overview of survey papers across many subfields of machine learning — including deep learning, NLP, computer vision, graph ML, reinforcement learning, recommendation systems, embeddings, meta-learning, and more. Instead of diving into code or experiments, this repo gathers authoritative survey and review articles, summarizing the state-of-the-art, trends, challenges, and directions within each subdomain. For someone trying to get up to speed with a new ML subfield — say graph neural networks or meta-learning — ml-surveys offers a curated reading list of foundational and recent works, helping map the landscape quickly. It is particularly useful for researchers, data scientists, or engineers who want conceptual clarity about where a field stands, what problems remain, and what techniques are most established.
Features
- Curated list of survey and review papers across many ML subfields (NLP, CV, graphs, RL, embeddings, etc.)
- Organized by topic — making it easy to browse by area of interest (e.g. vision, graphs, recommendation)
- Helps newcomers or seasoned practitioners catch up on state-of-the-art and historical context
- Provides a high-level map of challenges, techniques, and research trends within ML domains
- Open-source under MIT license, allowing community contributions and updates
- Useful as reference for research, proposal writing, learning, or designing new ML projects
Project Samples
