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To improve how neural networks function it is crucial to understand their learning process.
Error-based and entropy-based discretization of continuous features
Ron Kohavi and Mehran Sahami · 1996
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The information bottleneck method
Naftali Tishby, Fernando C. N. Pereira, and William Bialek · 2000
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Extracting relevant structures with side information
Gal Chechik and Naftali Tishby · 2002
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E. Hinton · 2010
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
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Fast and accurate deep network learning by exponential linear units (elus), 2016
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2016
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Systematic evaluation of CNN advances on the imagenet
Dmytro Mishkin, Nikolay Sergievskiy, and Jiri Matas · 2016
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Estimating mixture entropy with pairwise distances
Artemy Kolchinsky and Brendan D. Tracey · 2017
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Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V. Le · 2017
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Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, L. Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis · 2017
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Understanding deep learning requires rethinking generalization, 2017
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Information dropout: Learning optimal representations through noisy computation, 2017
Alessandro Achille and Stefano Soatto · 2017
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2017
Cited alongside, same era.
On the importance of single directions for generalization
Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, and Matthew Botvinick · 2018
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On the information bottleneck theory of deep learning
Andrew Michael Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan Daniel Tracey, and David Daniel Cox · 2018
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