Awesome - Most Cited Deep Learning Papers

updated: 2/17/2017


A curated list of the most cited deep learning papers (since 2012)

We believe that there exist classic deep learning papers which are worth reading regardless of their application domain. Rather than providing overwhelming amount of papers, We would like to provide a curated list of the awesome deep learning papers which are considered as must-reads in certain research domains.


Before this list, there exist other awesome deep learning lists, for example, Deep Vision and Awesome Recurrent Neural Networks. Also, after this list comes out, another awesome list for deep learning beginners. called Deep Learning Papers Reading Roadmap, has been created and loved by many deep learning researchers.

Although the Roadmap List includes lots of important deep learning papers, it feels overwhelming for me to read them all. As I mentioned in the introduction, I believe that seminal works can give us lessons regardless of their application domain. Thus, I would like to introduce top 100 deep learning papers here as a good starting point of overviewing deep learning researches.

Awesome list criteria

  1. A list of top 100 deep learning papers published from 2012 to 2016 is suggested.
  2. If a paper is added to the list, another paper (usually from *More Papers from 2016" section) should be removed to keep top 100 papers. (Thus, removing papers is also important contributions as well as adding papers)
  3. Papers that are important, but failed to be included in the list, will be listed in More than Top 100 section.
  4. Please refer to New Papers and Old Papers sections for the papers published in recent 6 months or before 2012.

(Citation criteria)

  • < 6 months : New Papers (by discussion)
  • 2016 : +60 citations or "More Papers from 2016"
  • 2015 : +200 citations
  • 2014 : +400 citations
  • 2013 : +600 citations
  • 2012 : +800 citations
  • ~2012 : Old Papers (by discussion)

Please note that we prefer seminal deep learning papers that can be applied to various researches rather than application papers. For that reason, some papers that meet the criteria may not be accepted while others can be. It depends on the impact of the paper, applicability to other researches scarcity of the research domain, and so on.

We need your contributions!

If you have any suggestions (missing papers, new papers, key researchers or typos), please feel free to edit and pull a request.
(Please read the contributing guide for further instructions, though just letting me know the title of papers can also be a big contribution to us.)

Table of Contents

(More than Top 100)

Understanding / Generalization / Transfer

<!---[Key researchers] Geoffrey Hinton, Yoshua Bengio, Jason Yosinski -->

Optimization / Training Techniques

<!---[Key researchers] Geoffrey Hinton, Yoshua Bengio, Christian Szegedy, Sergey Ioffe, Kaming He, Diederik P. Kingma-->

Unsupervised / Generative Models

Convolutional Neural Network Models

<!---[Key researchers] Christian Szegedy, Kaming He, Shaoqing Ren, Jian Sun, Geoffrey Hinton, Yoshua Bengio, Yann LeCun-->

Image: Segmentation / Object Detection

<!---[Key researchers] Ross Girshick, Jeff Donahue, Trevor Darrell-->

Image / Video / Etc

<!---[Key researchers] Oriol Vinyals, Andrej Karpathy-->

Recurrent Neural Network Models

<!---[Key researchers] Alex Graves-->

Natural Language Process

<!---[Key researchers] Kyunghyun Cho, Oriol Vinyals, Richard Socher, Tomas Mikolov, Christopher D. Manning, Yoshua Bengio-->

Speech / Other Domain

<!---[Key researchers] Alex Graves, Geoffrey Hinton, Dong Yu-->

Reinforcement Learning

<!---[Key researchers] Sergey Levine, Volodymyr Mnih, David Silver-->

More Papers from 2016

New papers

Newly published papers (< 6 months) which are worth reading

Old Papers

Classic papers published before 2012

HW / SW / Dataset

Book / Survey / Review

Video Lectures / Tutorials / Blogs




Appendix: More than Top 100




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