# terryum/awesome-deep-learning-papers

The most cited deep learning papers

Repository: https://github.com/terryum/awesome-deep-learning-papers
Canonical: https://ross.abutalabs.com/products/awesome-deep-learning-papers
Language: TeX
License Family: other
Topics: deep-learning, deep-neural-networks, machine-learning
Last push: 2024-01-18T13:29:44+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": 3743, "days_push": 958, "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 26183, forks 4416 (observed 2026-08-28T04:11:38.850739+00:00)

## What it is
A curated awesome list of the top 100 most-cited deep learning papers from 2012-2016, organized as must-read classics across research domains. It is no longer maintained due to the volume of new papers since 2017.

## Use cases
- find must-read deep learning papers
- get a starting reading list for deep learning research
- discover seminal neural network papers
- learn deep learning fundamentals through classic papers
- find most cited machine learning papers

## When to choose
- you want a curated, citation-ranked list of foundational deep learning papers
- you are a beginner seeking a manageable starting point for deep learning literature
- you want classic pre-2017 papers regardless of application domain

## When to avoid
- you need coverage of recent deep learning research after 2016
- you want an actively maintained resource
- you need application-specific or domain-specific paper lists

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: documentation
- domain: deep-learning, machine-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, curated-papers, reading-list, research-papers

## Member repositories
- terryum/awesome-deep-learning-papers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:38.850739+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 2026-08-29T16:55:55.482083+00:00, confidence not recorded.
  - readme: https://github.com/terryum/awesome-deep-learning-papers (fetched 2026-08-28T04:11:38.850739+00:00, sha 8a530dd5ac8a)
- Data as of 2026-08-30T08:39:29.467469+00:00.