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Most cited deep learning papers

This is a curated list of the most cited deep learning papers (since 2012) posted by Terry Taewoong Um.

Source for picture: What is deep learning and how does it work?

The repository is broken down into the following categories:

  • Understanding / Generalization / Transfer
  • Optimization / Training Techniques
  • Unsupervised / Generative Models
  • Convolutional Network Models
  • Image Segmentation / Object Detection
  • Image / Video / Etc
  • Recurrent Neural Network Models
  • Natural Language Process
  • Speech / Other Domain
  • Reinforcement Learning / Robotics
  • More Papers from 2016

For instance,  the first category contains the following articles:

  • Distilling the knowledge in a neural network (2015), G. Hinton et al. [pdf]
  • Deep neural networks are easily fooled: High confidence predictions for unrecognizable images (2015), A. Nguyen et al. [pdf]
  • How transferable are features in deep neural networks? (2014), J. Yosinski et al. [pdf]
  • CNN features off-the-Shelf: An astounding baseline for recognition (2014), A. Razavian et al. [pdf]
  • Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. [pdf]
  • Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus [pdf]
  • Decaf: A deep convolutional activation feature for generic visual recognition (2014), J. Donahue et al. [pdf]

Read the full list here

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