# arXivTimes/arXivTimes

repository to research & share the machine learning articles

Repository: https://github.com/arXivTimes/arXivTimes
Canonical: https://ross.abutalabs.com/products/arxivtimes
Homepage: https://arxivtimes.herokuapp.com/
License: MIT
License Family: permissive
Topics: machine-learning, natural-language-processing, computer-vision, reinforcement-learning, arxivtimes
Last push: 2022-07-01T13:15:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3598, "days_push": 1524, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3900, forks 198 (observed 2026-08-28T04:08:28.579500+00:00)

## What it is
A community repository for researching and sharing machine learning paper summaries, managed as GitHub Issues with one-line takeaways, links, and discussion. It also curates ML datasets, tools, and conference papers (NeurIPS, ICLR, ICML, CVPR, ACL, AAAI).

## Use cases
- find summaries of machine learning papers
- discover datasets for ML experiments
- keep up with new arXiv papers in NLP and computer vision
- find papers accepted at NeurIPS, ICML, or CVPR
- learn machine learning by reading curated paper notes
- find tools for implementing ML models

## When to choose
- you want concise, community-reviewed summaries of ML papers
- you are looking for curated lists of ML datasets and tools
- you read Japanese and want paper digests with discussion

## When to avoid
- you need runnable code or a software library rather than paper notes
- you need up-to-date summaries, since activity has slowed since 2022
- you prefer English-only resources

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, data-science
- domain: machine-learning, computer-vision, tutorials
- platform: -
- tags: arxiv, paper-summaries, research-papers, community, japanese, natural-language-processing, web-server

## Member repositories
- arXivTimes/arXivTimes (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:28.579500+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 2026-08-29T18:25:11.365727+00:00, confidence not recorded.
  - readme: https://github.com/arXivTimes/arXivTimes (fetched 2026-08-28T04:08:28.579500+00:00, sha 0b4a33ea780d)
- Data as of 2026-08-30T08:39:29.467469+00:00.