GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems.
Good fit when
- When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning
- For researchers or practitioners looking to quickly catch up on recent developments across various subfields of AI without reading each original paper
Avoid when
- If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary
- In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (1283d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 20, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/eugeneyan/ml-surveysSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A collection of survey papers that cover advancements and research in deep learning, natural language processing, computer vision, graphs, reinforcement learning, recommendations, among other areas within the field of artificial intelligence.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
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README
ml surveys It's hard to keep up with the latest and greatest in machine learning. Here's a selection of survey papers summarizing the advances in the field . Figuring out how to implement your ML project? Learn how other organizations did it 👉 Table of Contents Recommendation De...
For agents
This page has a .md twin and JSON over the API.