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"

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Title
Generalization Guarantees for Multi-View Representation Learning and Application to Regularization via Gaussian Product Mixture Prior Milad Sefidgaran Abdellatif Zaidi Piotr Krasnowski 44 0 0 25 Apr 2025
Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors Milad Sefidgaran A. Zaidi Piotr Krasnowski 83 1 0 21 Feb 2025
Task-Augmented Cross-View Imputation Network for Partial Multi-View Incomplete Multi-Label Classification Xiaohuan Lu Lian Zhao Wai Keung Wong Jie Wen Jiang Long Wulin Xie 33 1 0 12 Sep 2024
Enhancing Neural Network Interpretability Through Conductance-Based Information Plane Analysis J. Dabounou Amine Baazzouz FAtt 22 0 0 26 Aug 2024
10 Years of Fair Representations: Challenges and Opportunities Mattia Cerrato Marius Köppel Philipp Wolf Stefan Kramer FaML 31 1 0 04 Jul 2024
Representation Learning with Conditional Information Flow Maximization Dou Hu Lingwei Wei Wei Zhou Songlin Hu SSL 32 1 0 08 Jun 2024
Understanding Encoder-Decoder Structures in Machine Learning Using Information Measures Jorge F. Silva Victor Faraggi Camilo Ramírez Álvaro F. Egaña Eduardo Pavez 17 1 0 30 May 2024
Information-Theoretic Generalization Bounds for Deep Neural Networks Haiyun He Christina Lee Yu 35 4 0 04 Apr 2024
Information Plane Analysis Visualization in Deep Learning via Transfer Entropy Adrian Moldovan A. Cataron Razvan Andonie FAtt 19 1 0 01 Apr 2024
Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy Songjie Xie Youlong Wu Jiaxuan Li Ming Ding Khaled B. Letaief AAML 31 1 0 16 Feb 2024
End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training Keitaro Sakamoto Issei Sato 19 4 0 14 Feb 2024
Tighter Bounds on the Information Bottleneck with Application to Deep Learning Nir Weingarten Z. Yakhini Moshe Butman Ran Gilad-Bachrach AAML 11 1 0 12 Feb 2024
Minimum Description Length and Generalization Guarantees for Representation Learning Milad Sefidgaran Abdellatif Zaidi Piotr Krasnowski 38 7 0 05 Feb 2024
Flexible Variational Information Bottleneck: Achieving Diverse Compression with a Single Training Sota Kudo N. Ono Shigehiko Kanaya Ming Huang 11 1 0 02 Feb 2024
TURBO: The Swiss Knife of Auto-Encoders Guillaume Quétant Yury Belousov Vitaliy Kinakh S. Voloshynovskiy 24 6 0 11 Nov 2023
Multi-View Causal Representation Learning with Partial Observability Dingling Yao Danru Xu Sébastien Lachapelle Sara Magliacane Perouz Taslakian Georg Martius Julius von Kügelgen Francesco Locatello CML 37 30 0 07 Nov 2023
Self-MI: Efficient Multimodal Fusion via Self-Supervised Multi-Task Learning with Auxiliary Mutual Information Maximization Cam-Van Thi Nguyen Ngoc-Hoa Thi Nguyen Duc-Trong Le Quang-Thuy Ha SSL 24 0 0 07 Nov 2023
How Does Information Bottleneck Help Deep Learning? Kenji Kawaguchi Zhun Deng Xu Ji Jiaoyang Huang 44 53 0 30 May 2023
Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression I. Butakov Alexander Tolmachev S. Malanchuk A. Neopryatnaya Alexey Frolov K. Andreev 13 4 0 13 May 2023
To Compress or Not to Compress- Self-Supervised Learning and Information Theory: A Review Ravid Shwartz-Ziv Yann LeCun SSL 27 71 0 19 Apr 2023
Label Information Bottleneck for Label Enhancement Qinghai Zheng Jihua Zhu Haoyu Tang 18 6 0 13 Mar 2023
An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization Ravid Shwartz-Ziv Randall Balestriero Kenji Kawaguchi Tim G. J. Rudner Yann LeCun 30 23 0 01 Mar 2023
Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems Jirong Yi Q. Zhang Zhengbo Chen Qiaoan Liu Weizhuo Shao Yusen He Yao Wang SSL 28 0 0 23 Nov 2022
Defects of Convolutional Decoder Networks in Frequency Representation Ling Tang Wen Shen Zhanpeng Zhou YueFeng Chen Quanshi Zhang 25 14 0 17 Oct 2022
Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative Representations Haifeng Li Jun Cao Jiawei Zhu Qinyao Luo Silu He Xuying Wang 6 41 0 15 Oct 2022
Variational Graph Generator for Multi-View Graph Clustering Jianpeng Chen Yawen Ling Jie Xu Yazhou Ren Shudong Huang X. Pu Zhifeng Hao Philip S. Yu Lifang He 19 5 0 13 Oct 2022
Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Features Changde Du Kaicheng Fu Jinpeng Li Huiguang He VLM 29 67 0 13 Oct 2022
What Do We Maximize in Self-Supervised Learning? Ravid Shwartz-Ziv Randall Balestriero Yann LeCun SSL 23 17 0 20 Jul 2022
Towards Lightweight Super-Resolution with Dual Regression Learning Yong Guo Mingkui Tan Zeshuai Deng Jingdong Wang Qi Chen Jiezhang Cao Yanwu Xu Jian Chen SupR 16 11 0 16 Jul 2022
Exploring Adversarial Examples and Adversarial Robustness of Convolutional Neural Networks by Mutual Information Jiebao Zhang Wenhua Qian Ren-qi Nie Jinde Cao Dan Xu GAN AAML 17 0 0 12 Jul 2022
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility Behrooz Razeghi Flavio du Pin Calmon Deniz Gunduz S. Voloshynovskiy 27 15 0 11 Jul 2022
InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle Mengting Xu Tao Zhang Zhongnian Li Daoqiang Zhang AAML 35 16 0 23 Jun 2022
Batch Normalization Is Blind to the First and Second Derivatives of the Loss Zhanpeng Zhou Wen Shen Huixin Chen Ling Tang Quanshi Zhang 26 2 0 30 May 2022
Optimal Randomized Approximations for Matrix based Renyi's Entropy Yuxin Dong Tieliang Gong Shujian Yu Chen Li 19 7 0 16 May 2022
Multi-view Information Bottleneck Without Variational Approximation Qi Zhang Shujian Yu J. Xin Badong Chen 12 10 0 22 Apr 2022
R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction Yu Wang Shuo Ye Shujian Yu Xinge You 22 14 0 21 Apr 2022
Studying the Interplay between Information Loss and Operation Loss in Representations for Classification Jorge F. Silva Felipe A. Tobar Mario L. Vicuna Felipe Cordova 9 2 0 30 Dec 2021
Computationally Efficient Approximations for Matrix-based Renyi's Entropy Tieliang Gong Yuxin Dong Shujian Yu B. Dong 59 2 0 27 Dec 2021
PACMAN: PAC-style bounds accounting for the Mismatch between Accuracy and Negative log-loss Matías Vera L. Rey Vega Pablo Piantanida 23 0 0 10 Dec 2021
Discovering and Explaining the Representation Bottleneck of DNNs Huiqi Deng Qihan Ren Hao Zhang Quanshi Zhang 37 59 0 11 Nov 2021
Gated Information Bottleneck for Generalization in Sequential Environments Francesco Alesiani Shujian Yu Xi Yu OOD AAML 11 13 0 12 Oct 2021
Ranking Feature-Block Importance in Artificial Multiblock Neural Networks Anna Jenul Stefan Schrunner B. Huynh Runar Helin C. Futsaether K. H. Liland O. Tomic FAtt 8 1 0 21 Sep 2021
Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis Wei Han Hui Chen Soujanya Poria 19 315 0 01 Sep 2021
Learning to Transfer with von Neumann Conditional Divergence Ammar Shaker Shujian Yu Daniel Oñoro-Rubio OOD DRL 17 1 0 07 Aug 2021
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks S. Lorenzen Christian Igel M. Nielsen MQ 11 17 0 24 Jun 2021
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks Hengrui Zhang Qitian Wu Junchi Yan David Wipf Philip S. Yu SSL 22 210 0 23 Jun 2021
Optimization Induced Equilibrium Networks Xingyu Xie Qiuhao Wang Zenan Ling Xia Li Yisen Wang Guangcan Liu Zhouchen Lin 11 9 0 27 May 2021
Disentangled Variational Information Bottleneck for Multiview Representation Learning Feng Bao 11 12 0 17 May 2021
Understanding Neural Networks with Logarithm Determinant Entropy Estimator Zhanghao Zhouyin Ding Liu FAtt 16 8 0 08 May 2021
A Critical Review of Information Bottleneck Theory and its Applications to Deep Learning Mohammad Ali Alomrani 8 2 0 07 May 2021