# Awesome Deep Learning Papers

> An awesome collection of the most-cited and influential deep learning papers, organized as a study and reference list.

- **URL:** https://github.com/terryum/awesome-deep-learning-papers#readme
- **Category:** Themed Directories
- **Tags:** Deep Learning, Research, Awesome Lists
- **Updated:** 2026-04-23 14:34
- **Canonical page:** https://dir.ever.works/items/awesome-deep-learning-papers

## Details

# Awesome Deep Learning Papers

**URL:** https://github.com/terryum/awesome-deep-learning-papers#readme  
**Category:** Themed Directories  
**Tags:** deep-learning, research, awesome-lists

## Description
Awesome Deep Learning Papers is a curated directory of the most-cited and influential deep learning research papers, primarily from 2012–2016. It focuses on seminal works that are considered must-reads for gaining an overview of key developments in deep learning. The list is no longer actively maintained due to the rapid growth of the field.

## Features
- **Curated top-100 list (2012–2016):**
  - Focus on the top 100 most-cited and influential deep learning papers published between 2012 and 2016.
  - Emphasis on classic, seminal works rather than a comprehensive or exhaustive catalog.

- **Citation-based inclusion criteria:**
  - Papers are evaluated using citation thresholds and impact:
    - < 6 months old: categorized as *New Papers* (selected by discussion).
    - 2016: ≥ 60 citations or included in a "More Papers from 2016" section.
    - 2015: ≥ 200 citations.
    - 2014: ≥ 400 citations.
    - 2013: ≥ 600 citations.
    - 2012: ≥ 800 citations.
    - Pre-2012: considered *Old Papers* (selected by discussion).

- **Focused on general, seminal contributions:**
  - Prioritizes papers whose ideas can be applied across multiple research domains.
  - De-emphasizes narrow application papers, even if highly cited.
  - Selection depends on impact, applicability to other research, and coverage of less-populated research areas.

- **Structured sections for paper organization:**
  - **Top 100 Papers:** main curated list.
  - **More than Top 100:** for important works that did not fit into the top-100 limit.
  - **New Papers:** recent works (last ~6 months at the time of listing).
  - **Old Papers:** influential pre-2012 works.
  - **More Papers from 2016:** an overflow section for noteworthy 2016 papers.

- **Dynamic curation rules:**
  - The list is strictly capped at 100 main papers.
  - Adding a new paper requires removing another (often from the "More Papers from 2016" section).
  - Removal of papers is treated as a meaningful contribution to maintain quality and focus.

- **Context within the ecosystem of deep learning lists:**
  - References and complements other themed lists such as:
    - *Deep Vision* (awesome deep vision papers).
    - *Awesome Recurrent Neural Networks*.
    - *Deep Learning Papers Reading Roadmap* (a broader reading sequence for beginners and researchers).
  - Positions itself as a concise, non-overwhelming starting point for understanding core deep learning literature.

- **Community contributions (GitHub-based):**
  - Open to contributions via pull requests for:
    - Missing or newly important papers.
    - Corrections and updates.
    - Suggestions of key researchers.
  - A dedicated contributing guide is provided in the repository.

- **Automation scripts and resources:**
  - Script to **download all top-100 papers** (`fetch_papers.py`).
  - Script to **collect all authors’ names** from the top-100 papers.

- **Maintenance status:**
  - Marked as **no longer maintained** due to the overwhelming volume of deep learning papers being published since 2017.

## Pricing
Not applicable. This is a free, open GitHub-based directory of research papers.

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