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The HSIC Bottleneck: Deep Learning without Back-Propagation

5 August 2019

Papers citing "The HSIC Bottleneck: Deep Learning without Back-Propagation"

33 / 33 papers shown

Title
Training neural networks without backpropagation using particles Deepak Kumar 133 0 0 07 Dec 2024
FLOPS: Forward Learning with OPtimal Sampling Tao Ren Zishi Zhang Jinyang Jiang Guanghao Li Zeliang Zhang Mingqian Feng Yijie Peng 132 1 0 08 Oct 2024
LIX: Implicitly Infusing Spatial Geometric Prior Knowledge into Visual Semantic Segmentation for Autonomous Driving Sicen Guo Zhiyuan Wu Qijun Chen Ioannis Pitas Rui Fan Rui Fan 94 1 0 13 Mar 2024
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness Xiaoyun Xu Shujian Yu Jingzheng Wu S. Picek AAML 84 0 0 08 Dec 2023
An Information Theory-inspired Strategy for Automatic Network Pruning Xiawu Zheng Yuexiao Ma Teng Xi Gang Zhang Errui Ding Yuchao Li Jie Chen Yonghong Tian Rongrong Ji 179 13 0 19 Aug 2021
Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks Roman Pogodin P. Latham 140 35 0 12 Jun 2020
On Mutual Information Maximization for Representation Learning Michael Tschannen Josip Djolonga Paul Kishan Rubenstein Sylvain Gelly Mario Lucic SSL 179 501 0 31 Jul 2019
Feedback alignment in deep convolutional networks Theodore H. Moskovitz Ashok Litwin-Kumar L. F. Abbott 61 61 0 12 Dec 2018
Biologically-plausible learning algorithms can scale to large datasets Y. Chitour Honglin Chen Zhenyu Liao T. Poggio 73 76 0 08 Nov 2018
A Biologically Plausible Learning Rule for Deep Learning in the Brain Isabella Pozzi Michael Felsberg Fahad Shahbaz Khan AI4CE 46 31 0 05 Nov 2018
The Variational Deficiency Bottleneck P. Banerjee Guido Montúfar 132 7 0 27 Oct 2018
Gradient target propagation T. S. Farias Jonas Maziero 28 3 0 19 Oct 2018
Estimating Information Flow in Deep Neural Networks Ziv Goldfeld E. Berg Kristjan Greenewald Igor Melnyk Nam H. Nguyen Brian Kingsbury Yury Polyanskiy 63 32 0 12 Oct 2018
Error Forward-Propagation: Reusing Feedforward Connections to Propagate Errors in Deep Learning Adam A. Kohan E. Rietman H. Siegelmann 85 24 0 09 Aug 2018
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures Sergey Bartunov Adam Santoro Blake A. Richards Luke Marris Geoffrey E. Hinton Timothy Lillicrap 92 244 0 12 Jul 2018
Beyond Backprop: Online Alternating Minimization with Auxiliary Variables A. Choromańska Benjamin Cowen Yara Rizk Ronny Luss Mattia Rigotti ... Brian Kingsbury Paolo Diachille V. Gurev Ravi Tejwani Djallel Bouneffouf 75 54 0 24 Jun 2018
Information Constraints on Auto-Encoding Variational Bayes Romain Lopez Jeffrey Regier Michael I. Jordan N. Yosef BDL 71 124 0 22 May 2018
"Dependency Bottleneck" in Auto-encoding Architectures: an Empirical Study Denny Wu Yixiu Zhao Yao-Hung Hubert Tsai M. Yamada Ruslan Salakhutdinov 43 10 0 15 Feb 2018
Intriguing Properties of Randomly Weighted Networks: Generalizing While Learning Next to Nothing Amir Rosenfeld John K. Tsotsos MLT 65 51 0 02 Feb 2018
Learning Independent Features with Adversarial Nets for Non-linear ICA Philemon Brakel Yoshua Bengio OOD CML 63 94 0 13 Oct 2017
Nonlinear Information Bottleneck Artemy Kolchinsky Brendan D. Tracey David Wolpert 55 156 0 06 May 2017
Opening the Black Box of Deep Neural Networks via Information Ravid Shwartz-Ziv Naftali Tishby AI4CE 111 1,416 0 02 Mar 2017
Deep Variational Information Bottleneck Alexander A. Alemi Ian S. Fischer Joshua V. Dillon Kevin Patrick Murphy 128 1,728 0 01 Dec 2016
A Powerful Generative Model Using Random Weights for the Deep Image Representation Kun He Yan Wang John E. Hopcroft 110 77 0 15 Jun 2016
Towards Biologically Plausible Deep Learning Yoshua Bengio Dong-Hyun Lee J. Bornschein Thomas Mesnard Zhouhan Lin DRL OOD 82 352 0 14 Feb 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 465 43,341 0 11 Feb 2015
Kickback cuts Backprop's red-tape: Biologically plausible credit assignment in neural networks David Balduzzi Hastagiri P. Vanchinathan J. M. Buhmann FAtt 71 71 0 23 Nov 2014
How transferable are features in deep neural networks? J. Yosinski Jeff Clune Yoshua Bengio Hod Lipson OOD 234 8,348 0 06 Nov 2014
Random feedback weights support learning in deep neural networks Timothy Lillicrap D. Cownden D. Tweed C. Akerman ODL 72 171 0 02 Nov 2014
CNN Features off-the-shelf: an Astounding Baseline for Recognition A. Razavian Hossein Azizpour Josephine Sullivan S. Carlsson 159 4,945 0 23 Mar 2014
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition Jeff Donahue Yangqing Jia Oriol Vinyals Judy Hoffman Ning Zhang Eric Tzeng Trevor Darrell VLM ObjD 191 4,950 0 06 Oct 2013
Hypothesis testing using pairwise distances and associated kernels (with Appendix) Dino Sejdinovic Arthur Gretton Bharath K. Sriperumbudur Kenji Fukumizu 124 39 0 02 May 2012
Universality, Characteristic Kernels and RKHS Embedding of Measures Bharath K. Sriperumbudur Kenji Fukumizu Gert R. G. Lanckriet 224 531 0 03 Mar 2010