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The Information Bottleneck (IB) method (\cite{tishby2000information}) provides an insightful and principled approach for balancing compression and prediction for representation learning.
A connection between correlation and contingency
Hermann O Hirschfeld · 1935
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Das statistische problem der korrelation als variations-und eigenwertproblem und sein zusammenhang mit der ausgleichsrechnung
Hans Gebelein · 1941
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A Mathematical Theory of Communication
Claude Elwood Shannon · 1948
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On measures of dependence
Alfréd Rényi · 1959
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Emergence of invariance and disentanglement in deep representations
Alessandro Achille and Stefano Soatto · 1980
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Learning from noisy examples
Dana Angluin and Philip Laird · 1988
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The efficiency of investment information
Elza Erkip and Thomas M Cover · 1998
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Calculus of variations
Izrail Moiseevitch Gelfand, Richard A Silverman, et al · 2000
Earlier work this paper cites.
The information bottleneck method
Naftali Tishby, Fernando C Pereira, and William Bialek · 2000
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Estimating mutual information
Alexander Kraskov, Harald Stögbauer, and Peter Grassberger · 2004
Earlier work this paper cites.
Information bottleneck for gaussian variables
Gal Chechik, Amir Globerson, Naftali Tishby, and Yair Weiss · 2005
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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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Meta-gaussian information bottleneck
Mélanie Rey and Volker Roth · 2012
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Venkat Anantharam, Amin Gohari, Sudeep Kamath, and Chandra Nair · 2013
Cited alongside, same era.
Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
Cited alongside, same era.
Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 2016
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Conditional Image Generation with PixelCNN Decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, and Alex Graves · 2016
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S. Zagoruyko and N. Komodakis · 2016
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Discovering potential correlations via hypercontractivity
Hyeji Kim, Weihao Gao, Sreeram Kannan, Sewoong Oh, and Pramod Viswanath · 2017
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Learning with confident examples: Rank pruning for robust classification with noisy labels
Curtis G Northcutt, Tailin Wu, and Isaac L Chuang · 2017
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Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
Cited alongside, same era.
Deep variational information bottleneck
Alexander A Alemi, Ian Fischer, Joshua V Dillon, and Kevin Murphy · 2016
Cited alongside, same era.
Relevant sparse codes with variational information bottleneck
Matthew Chalk, Olivier Marre, and Gasper Tkacik · 2016
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Criticality in formal languages and statistical physics
Henry W Lin and Max Tegmark · 2016
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Information dropout: Learning optimal representations through noisy computation
Alessandro Achille and Stefano Soatto
Cited in the paper.
Strong data-processing inequalities for channels and bayesian networks
Yury Polyanskiy and Yihong Wu · 2017
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PixelCNN++: A PixelCNN Implementation with Discretized Logistic Mixture Likelihood and Other Modifications
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P. Kingma · 2017
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The Dynamics of Differential Learning I: Information-Dynamics and Task Reachability
Alessandro Achille, Glen Mbeng, and Stefano Soatto · 2018
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Autoaugment: Learning augmentation policies from data
Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le · 2018
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The conditional entropy bottleneck, 2018
Ian Fischer · 2018
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Caveats for information bottleneck in deterministic scenarios
Artemy Kolchinsky, Brendan D Tracey, and Steven Van Kuyk · 2019
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