# zhaoyang97/Paper-Notes

📚 数千篇 AI、LLM、NLP、CV 顶会论文解读，每篇 5 分钟读懂核心思想。

Repository: https://github.com/zhaoyang97/Paper-Notes
Canonical: https://ross.abutalabs.com/products/paper-notes
Homepage: https://papernotes.org
Language: Python
License: NOASSERTION
License Family: other
Topics: paper-notes, paper-reading, ai, cv, llm, nlp, paper, acl, cvpr, iclr, neurips, acl2026, cvpr2026, icml-2026
Last push: 2026-08-02T05:00:47+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 95, release rhythm 35, longevity 12
- inputs: {"age_days": 168, "days_push": 31, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1628, forks 72 (observed 2026-08-28T04:05:13.740748+00:00)

## What it is
A curated collection of 23,000+ concise (5-minute) summaries of AI, LLM, NLP, and computer vision papers from top conferences like CVPR, ICLR, NeurIPS, ACL, ICML, and ECCV, browsable online at papernotes.org. The repository also publishes per-conference paper lists with links to each note.

## Use cases
- quickly understand the core idea of a top-conference AI paper
- catch up on the latest LLM and computer vision research
- find papers on a specific topic like RAG, RLHF, or image generation
- browse complete paper lists for CVPR 2026 or ICLR 2026
- stay current with AI conference output without reading full papers
- find reading material for a research literature review

## When to choose
- you need fast, digestible summaries of recent AI conference papers
- you want broad coverage across LLM, NLP, and CV in one place
- you are surveying a research area like VLM, agents, or 3D vision
- you prefer reading in a browser with organized topic categories

## When to avoid
- you need full paper text, code, or rigorous peer review of the summaries
- you require notes for venues or years not yet covered
- you need a searchable academic database with citation metadata
- you want deep mathematical derivations rather than high-level takeaways

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, nlp, machine-learning, deep-learning, computer-vision
- domain: artificial-intelligence, large-language-models, computer-vision, tutorials, education
- platform: cross-platform
- tags: paper-notes, paper-summaries, academic-conferences, cvpr, iclr, neurips, acl, icml, eccv, aaai, llm-reasoning, rag, rlhf, aigc, research, natural-language-processing, web-server

## Member repositories
- zhaoyang97/Paper-Notes (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:13.740748+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:48:21.282951+00:00, confidence not recorded.
  - readme: https://github.com/zhaoyang97/Paper-Notes (fetched 2026-08-28T04:05:13.740748+00:00, sha 8869f7a3df07)
  - homepage: https://papernotes.org (fetched 2026-08-29T11:20:59.704550+00:00, sha 830a8f35b5ca)
- Data as of 2026-08-30T08:39:29.467469+00:00.