# robertsdionne/neural-network-papers

Repository: https://github.com/robertsdionne/neural-network-papers
Canonical: https://ross.abutalabs.com/products/neural-network-papers
Language: JavaScript
License Family: other
Topics: awesome-lists
Last push: 2020-07-19T08:17:52+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4014, "days_push": 2236, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1995, forks 373 (observed 2026-08-28T04:06:03.421817+00:00)

## What it is
A curated list of neural network and deep learning research papers, organized by topic such as CNNs, RNNs, GANs, reinforcement learning, and training techniques. It also links to surveys, books, datasets, pretrained models, and other resource lists.

## Use cases
- find deep learning research papers on a topic
- reading list for learning neural networks
- find papers on GANs or RNNs
- locate datasets and pretrained models for deep learning
- survey the deep learning literature before starting research

## When to choose
- you want a topic-organized bibliography of neural network papers
- you are building a self-study curriculum in deep learning
- you need pointers to related awesome lists, books, and datasets

## When to avoid
- you need runnable code or a library rather than paper links
- you need up-to-date papers covering recent years
- you want peer-reviewed curation rather than a community list

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation
- domain: deep-learning, machine-learning, artificial-intelligence, awesome-lists
- platform: -
- tags: curated-list, papers, bibliography, neural-networks, research, web-server

## Member repositories
- robertsdionne/neural-network-papers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:03.421817+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 2026-08-30T03:02:31.566191+00:00, confidence not recorded.
  - readme: https://github.com/robertsdionne/neural-network-papers (fetched 2026-08-28T04:06:03.421817+00:00, sha 4a1b0b40ceca)
- Data as of 2026-08-30T08:39:29.467469+00:00.