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Analyzing deep neural networks (DNNs) via information plane (IP) theory has gained tremendous attention recently as a tool to gain insight into, among others, their generalization ability.
A mathematical theory of communication
C. E. Shannon · 1948
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On measures of entropy and information
Alfred Renyi · 1961
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On estimation of a probability density function and mode
Emanuel Parzen · 1962
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Density Estimation for Statistics and Data Analysis , volume 26
Bernard W Silverman · 1986
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Normalized cuts and image segmentation
Jianbo Shi and Jitendra Malik · 2000
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On kernel-target alignment
Nello Cristianini, John Shawe-Taylor, Andre Elisseeff, and Jaz S Kandola · 2002
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Input feature selection by mutual information based on parzen window
N. Kwak and Chong-Ho Choi · 2002
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Understanding convolutional neural network training with information theory
Shujian Yu, Kristoffer Wickstrøm, Robert Jenssen, and José C. Príncipe · 2002
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Infinitely divisible matrices
Rajendra Bhatia · 2006
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Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Thomas M. Cover and Joy A. Thomas · 2006
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Data spectroscopy: Eigenspaces of convolution operators and clustering
Tao Shi, Mikhail Belkin, Bin Yu, et al · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Information Theoretic Learning: Renyi’s Entropy and Kernel Perspectives
Jose C. Principe · 2010
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Quantum Computation and Quantum Information: 10th Anniversary Edition
Michael A. Nielsen and Isaac L. Chuang · 2011
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A kernel-based framework to tensorial data analysis
Marco Signoretto, Lieven De Lathauwer, and Johan AK Suykens · 2011
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Measures of entropy from data using infinitely divisible kernels
Luis Gonzalo Sanchez Giraldo, Murali Rao, and José Carlos Príncipe · 2012
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A kernel two-sample test
Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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Information theoretic learning with infinitely divisible kernels
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 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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Evaluating capability of deep neural networks for image classification via information plane
Hao Cheng, Dongze Lian, Shenghua Gao, and Yanlin Geng · 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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Learning representations for neural network-based classification using the information bottleneck principle
R. A. Amjad and B. C. Geiger · 2019
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Luis Gonzalo Sánchez Giraldo and José C. Príncipe · 2013
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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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deep learning and the information bottleneck principle
N. Tishby and N. Zaslavsky · 2015
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Kernel mean embedding of distributions: A review and beyond
Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, and Bernhard Schölkopf · 2017
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Adaptive estimators show information compression in deep neural networks
Ivan Chelombiev, Conor Houghton, and Cian O’Donnell · 2019
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Estimating information flow in deep neural networks
Ziv Goldfeld, Ewout Van Den Berg, Kristjan Greenewald, Igor Melnyk, Nam Nguyen, Brian Kingsbury, and Yury Polyanskiy · 2019
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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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Scalable mutual information estimation using dependence graphs
M. Noshad, Y. Zeng, and A. O. Hero · 2019
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Multivariate extension of matrix-based renyi’s α-order entropy functional
S. Yu, L. G. Sanchez Giraldo, R. Jenssen, and J. C. Principe · 2019
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Understanding autoencoders with information theoretic concepts
Shujian Yu and Jose C. Principe · 2019
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