Understanding Convolutional Neural Networks with Information Theory: An Initial Exploration

18 April 2018

Papers citing "Understanding Convolutional Neural Networks with Information Theory: An Initial Exploration"

35 / 35 papers shown

Title
Quantifying Privacy Leakage in Split Inference via Fisher-Approximated Shannon Information Analysis Ruijun Deng Zhihui Lu Qiang Duan FedML 46 0 0 14 Apr 2025
Cauchy-Schwarz Divergence Information Bottleneck for Regression Shujian Yu Xi Yu Sigurd Løkse Robert Jenssen José C. Príncipe UQCV 37 5 0 27 Apr 2024
Knowledge Distillation Based on Transformed Teacher Matching Kaixiang Zheng En-Hui Yang 32 19 0 17 Feb 2024
Wavelet Dynamic Selection Network for Inertial Sensor Signal Enhancement Yifeng Wang Yi Zhao 22 5 0 29 Dec 2023
MOLE: MOdular Learning FramEwork via Mutual Information Maximization Tianchao Li Yulong Pei 33 0 0 15 Aug 2023
Information Bottleneck Analysis of Deep Neural Networks via Lossy Compression I. Butakov Alexander Tolmachev S. Malanchuk A. Neopryatnaya Alexey Frolov K. Andreev 24 5 0 13 May 2023
New Adversarial Image Detection Based on Sentiment Analysis Yulong Wang Tianxiang Li Shenghong Li Xinnan Yuan W. Ni AAML 27 9 0 03 May 2023
Training Invertible Neural Networks as Autoencoders The-Gia Leo Nguyen Lynton Ardizzone Ullrich Kothe BDL DRL SSL 30 9 0 20 Mar 2023
Filter Pruning based on Information Capacity and Independence Xiaolong Tang Shuo Ye Yufeng Shi Tianheng Hu Qinmu Peng Xinge You VLM 37 0 0 07 Mar 2023
Higher-order mutual information reveals synergistic sub-networks for multi-neuron importance Kenzo Clauw S. Stramaglia Daniele Marinazzo SSL FAtt 30 6 0 01 Nov 2022
Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks A. Proca F. Rosas A. Luppi D. Bor Matthew Crosby P. Mediano 30 21 0 06 Oct 2022
A Measure of the Complexity of Neural Representations based on Partial Information Decomposition David A. Ehrlich Andreas C. Schneider V. Priesemann Michael Wibral Abdullah Makkeh 11 18 0 21 Sep 2022
Mutual information estimation for graph convolutional neural networks Marius Cervera Landsverk S. Riemer-Sørensen SSL GNN 22 1 0 31 Mar 2022
HRel: Filter Pruning based on High Relevance between Activation Maps and Class Labels C. Sarvani Mrinmoy Ghorai S. Dubey S. H. Shabbeer Basha VLM 39 37 0 22 Feb 2022
Computationally Efficient Approximations for Matrix-based Renyi's Entropy Tieliang Gong Yuxin Dong Shujian Yu B. Dong 67 2 0 27 Dec 2021
Information Theoretic Representation Distillation Roy Miles Adrian Lopez-Rodriguez K. Mikolajczyk MQ 13 21 0 01 Dec 2021
Disentanglement Analysis with Partial Information Decomposition Seiya Tokui Issei Sato CoGe DRL 20 15 0 31 Aug 2021
A Reflection on Learning from Data: Epistemology Issues and Limitations Ahmad Hammoudeh Sara Tedmori Nadim Obeid 16 3 0 28 Jul 2021
Rethinking Hard-Parameter Sharing in Multi-Domain Learning Lijun Zhang Qizheng Yang Xiao Liu Hui Guan OOD 31 14 0 23 Jul 2021
Information Bottleneck: Exact Analysis of (Quantized) Neural Networks S. Lorenzen Christian Igel M. Nielsen MQ 11 17 0 24 Jun 2021
Understanding Neural Networks with Logarithm Determinant Entropy Estimator Zhanghao Zhouyin Ding Liu FAtt 16 8 0 08 May 2021
InfoNEAT: Information Theory-based NeuroEvolution of Augmenting Topologies for Side-channel Analysis R. Acharya F. Ganji Domenic Forte AAML 38 24 0 30 Apr 2021
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges Yoshitomo Matsubara Marco Levorato Francesco Restuccia 33 199 0 08 Mar 2021
Deep Deterministic Information Bottleneck with Matrix-based Entropy Functional Xi Yu Shujian Yu José C. Príncipe AAML 19 26 0 31 Jan 2021
The distance between the weights of the neural network is meaningful Liqun Yang Yijun Yang Yao Wang Zhenyu Yang Wei Zeng 6 0 0 31 Jan 2021
Measuring Dependence with Matrix-based Entropy Functional Shujian Yu Francesco Alesiani Xi Yu Robert Jenssen José C. Príncipe 21 24 0 25 Jan 2021
A Probabilistic Representation of Deep Learning for Improving The Information Theoretic Interpretability Xinjie Lan Kenneth Barner FAtt 16 2 0 27 Oct 2020
PRI-VAE: Principle-of-Relevant-Information Variational Autoencoders Yanjun Li Shujian Yu José C. Príncipe Xiaolin Li D. Wu DRL 15 7 0 13 Jul 2020
On Information Plane Analyses of Neural Network Classifiers -- A Review Bernhard C. Geiger 32 50 0 21 Mar 2020
Do Compressed Representations Generalize Better? Hassan Hafez-Kolahi S. Kasaei Mahdiyeh Soleymani-Baghshah 21 1 0 20 Sep 2019
Dimensionality compression and expansion in Deep Neural Networks Stefano Recanatesi M. Farrell Madhu S. Advani Timothy Moore Guillaume Lajoie E. Shea-Brown 23 72 0 02 Jun 2019
Multivariate Extension of Matrix-based Renyi's α-order Entropy Functional Shujian Yu L. S. Giraldo Robert Jenssen José C. Príncipe 16 30 0 23 Aug 2018
Distributed Variational Representation Learning Iñaki Estella Aguerri Milad Sefidgaran 23 71 0 11 Jul 2018
Understanding Autoencoders with Information Theoretic Concepts Shujian Yu José C. Príncipe AI4CE 49 132 0 30 Mar 2018
The HASYv2 dataset Martin Thoma VLM 24 36 0 29 Jan 2017