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Recent works investigated the generalization properties in deep neural networks (DNNs) by studying the Information Bottleneck in DNNs.
The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
Earlier work this paper cites.
Scaling learning algorithms towards AI
Yoshua Bengio and Yann LeCun · 2007
Earlier work this paper cites.
A kernel statistical test of independence
Arthur Gretton, Kenji Fukumizu, Choon H Teo, Le Song, Bernhard Schölkopf, and Alex J Smola · 2008
Earlier work this paper cites.
Mutual information estimation reveals global associations between stimuli and biological processes
Taiji Suzuki, Masashi Sugiyama, Takafumi Kanamori, and Jun Sese · 2009
Earlier work this paper cites.
Elements of information theory
Thomas M Cover and Joy A Thomas · 2012
Cited alongside, same era.
On kernel parameter selection in hilbert-schmidt independence criterion
Masashi Sugiyama and Makoto Yamada · 2012
Cited alongside, same era.
Recognizing 50 human action categories of web videos
Kishore K Reddy and Mubarak Shah · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Measuring statistical dependence with hilbert-schmidt norms
Arthur Gretton, Olivier Bousquet, Alex Smola, and Bernhard Scholkopf
Cited in the paper.
Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
Later among the works it cites.
Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
Later among the works it cites.
On the information bottleneck theory of deep learning
Joel Dapello Madhu Advani Artemy Kolchinsky Brendan Daniel Tracey David Daniel Cox Andrew Michael Saxe, Yamini Bansal · 2018
Closest in time.
Kernel methods for measuring independence
Arthur Gretton, Ralf Herbrich, Alexander Smola, Olivier Bousquet, and Bernhard Schölkopf · 2075
Closest in time.
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