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]

(2015), G. Hinton et al. [pdf] Deep neural networks are easily fooled: High confidence predictions for unrecognizable images (2015), A. Nguyen et al. [pdf]

(2015), A. Nguyen et al. [pdf] How transferable are features in deep neural networks? (2014), J. Yosinski et al. [pdf]

(2014), J. Yosinski et al. [pdf] CNN features off-the-Shelf: An astounding baseline for recognition (2014), A. Razavian et al. [pdf]

(2014), A. Razavian et al. [pdf] Learning and transferring mid-Level image representations using convolutional neural networks (2014), M. Oquab et al. [pdf]

(2014), M. Oquab et al. [pdf] Visualizing and understanding convolutional networks (2014), M. Zeiler and R. Fergus [pdf]

(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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