ml-surveys

Why we included this project

Machine learning moves fast enough that a well-chosen survey is often the quickest way to get oriented. This is a hand-curated reading list of survey papers, each linked straight to the full paper, organized by subfield and then by specific topic: deep learning, NLP, computer vision, graph learning, reinforcement learning, recommendations, embeddings, and meta-learning. That two-level structure is the real draw: if you want knowledge distillation surveys or model compression, you can jump straight there instead of wading through a generic arXiv search. It works well as a study roadmap for practitioners and students who want breadth quickly, and the fine-grained subtopics make it easy to drill into a niche before committing to a deeper dive. It is a reading companion rather than a tool you run, which is exactly what you want for research exploration.