Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle

27 February 2018

Papers citing "Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle"

41 / 91 papers shown

Title
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs Devansh Bisla Apoorva Nandini Saridena A. Choromańska 23 8 0 05 May 2021
InfoNEAT: Information Theory-based NeuroEvolution of Augmenting Topologies for Side-channel Analysis R. Acharya F. Ganji Domenic Forte AAML 35 24 0 30 Apr 2021
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration Jaekyeom Kim Minjung Kim Dongyeon Woo Gunhee Kim 14 16 0 23 Mar 2021
Adversarial Information Bottleneck Penglong Zhai Shihua Zhang AAML 8 8 0 28 Feb 2021
Task-oriented Communication Design in Cyber-Physical Systems: A Survey on Theory and Applications Arsham Mostaani T. Vu Shree Krishna Sharma Van-Dinh Nguyen Qi Liao Symeon Chatzinotas 19 16 0 14 Feb 2021
Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional Xi Yu Shujian Yu José C. Príncipe AAML 11 26 0 31 Jan 2021
Measuring Dependence with Matrix-based Entropy Functional Shujian Yu Francesco Alesiani Xi Yu Robert Jenssen José C. Príncipe 11 24 0 25 Jan 2021
Disentangled Information Bottleneck Ziqi Pan Li Niu Jianfu Zhang Liqing Zhang 20 36 0 14 Dec 2020
Bottleneck Problems: Information and Estimation-Theoretic View S. Asoodeh Flavio du Pin Calmon 14 18 0 12 Nov 2020
Examining the causal structures of deep neural networks using information theory Simon Mattsson Eric J. Michaud Erik P. Hoel 6 9 0 26 Oct 2020
The Role of Mutual Information in Variational Classifiers Matías Vera L. Rey Vega Pablo Piantanida SSL DRL 23 2 0 22 Oct 2020
Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness Long Zhao Ting Liu Xi Peng Dimitris N. Metaxas OOD AAML 6 165 0 15 Oct 2020
Learning Optimal Representations with the Decodable Information Bottleneck Yann Dubois Douwe Kiela D. Schwab Ramakrishna Vedantam 10 43 0 27 Sep 2020
Learning to Learn with Variational Information Bottleneck for Domain Generalization Yingjun Du Jun Xu Huan Xiong Qiang Qiu Xiantong Zhen Cees G. M. Snoek Ling Shao BDL OOD 22 163 0 15 Jul 2020
On the Information Plane of Autoencoders Nicolás I. Tapia P. Estévez 12 19 0 15 May 2020
Renormalized Mutual Information for Artificial Scientific Discovery Leopoldo Sarra A. Aiello F. Marquardt 6 4 0 04 May 2020
The Information Bottleneck Problem and Its Applications in Machine Learning Ziv Goldfeld Yury Polyanskiy 13 129 0 30 Apr 2020
Why should we add early exits to neural networks? Simone Scardapane M. Scarpiniti E. Baccarelli A. Uncini 6 117 0 27 Apr 2020
Unpacking Information Bottlenecks: Unifying Information-Theoretic Objectives in Deep Learning Andreas Kirsch Clare Lyle Y. Gal 11 16 0 27 Mar 2020
On Information Plane Analyses of Neural Network Classifiers -- A Review Bernhard C. Geiger 24 50 0 21 Mar 2020
BiDet: An Efficient Binarized Object Detector Ziwei Wang Ziyi Wu Jiwen Lu Jie Zhou MQ 49 64 0 09 Mar 2020
The Conditional Entropy Bottleneck Ian S. Fischer OOD 19 115 0 13 Feb 2020
On the Estimation of Information Measures of Continuous Distributions Georg Pichler Pablo Piantanida Günther Koliander 14 13 0 07 Feb 2020
On the Information Bottleneck Problems: Models, Connections, Applications and Information Theoretic Views A. Zaidi Iñaki Estella Aguerri S. Shamai 6 89 0 31 Jan 2020
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learning Tailin Wu 19 1 0 11 Jan 2020
The Convex Information Bottleneck Lagrangian Borja Rodríguez Gálvez Ragnar Thobaben Mikael Skoglund 14 28 0 25 Nov 2019
Information in Infinite Ensembles of Infinitely-Wide Neural Networks Ravid Shwartz-Ziv Alexander A. Alemi 11 21 0 20 Nov 2019
Information Bottleneck Theory on Convolutional Neural Networks Jianing Li Ding Liu FAtt 20 3 0 09 Nov 2019
Information Plane Analysis of Deep Neural Networks via Matrix-Based Renyi's Entropy and Tensor Kernels Kristoffer Wickstrøm Sigurd Løkse Michael C. Kampffmeyer Shujian Yu José C. Príncipe Robert Jenssen 16 31 0 25 Sep 2019
Do Compressed Representations Generalize Better? Hassan Hafez-Kolahi S. Kasaei Mahdiyeh Soleymani-Baghshah 14 1 0 20 Sep 2019
Pareto-optimal data compression for binary classification tasks Max Tegmark Tailin Wu 11 16 0 23 Aug 2019
Feature selection of neural networks is skewed towards the less abstract cue Marcell Wolnitza B. Dellen 14 0 0 08 Aug 2019
Class-Conditional Compression and Disentanglement: Bridging the Gap between Neural Networks and Naive Bayes Classifiers Rana Ali Amjad Bernhard C. Geiger BDL 14 1 0 06 Jun 2019
Understanding the Behaviour of the Empirical Cross-Entropy Beyond the Training Distribution Matías Vera Pablo Piantanida L. Rey Vega 21 0 0 28 May 2019
Minimal Achievable Sufficient Statistic Learning Milan Cvitkovic Günther Koliander 17 12 0 19 May 2019
Wireless Network Intelligence at the Edge Jihong Park S. Samarakoon M. Bennis Mérouane Debbah 11 518 0 07 Dec 2018
Estimating Information Flow in Deep Neural Networks Ziv Goldfeld E. Berg Kristjan Greenewald Igor Melnyk Nam H. Nguyen Brian Kingsbury Yury Polyanskiy 17 32 0 12 Oct 2018
Caveats for information bottleneck in deterministic scenarios Artemy Kolchinsky Brendan D. Tracey S. Kuyk 13 81 0 23 Aug 2018
Understanding Neural Networks and Individual Neuron Importance via Information-Ordered Cumulative Ablation Rana Ali Amjad Kairen Liu Bernhard C. Geiger FAtt 11 18 0 18 Apr 2018
Layer-wise Learning of Stochastic Neural Networks with Information Bottleneck Thanh T. Nguyen Jaesik Choi 16 13 0 04 Dec 2017
Nonlinear Information Bottleneck Artemy Kolchinsky Brendan D. Tracey David Wolpert 12 152 0 06 May 2017