Variational Dropout Sparsifies Deep Neural Networks

19 January 2017

Dmitry Vetrov

Papers citing "Variational Dropout Sparsifies Deep Neural Networks"

26 / 126 papers shown

Title
Sparse Networks from Scratch: Faster Training without Losing Performance Tim Dettmers Luke Zettlemoyer 20 333 0 10 Jul 2019
Deep Active Learning with Adaptive Acquisition Manuel Haussmann Fred Hamprecht M. Kandemir 14 41 0 27 Jun 2019
Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes R. Krishnan Mahesh Subedar Omesh Tickoo BDL 15 46 0 12 Jun 2019
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness Adnan Siraj Rakin Zhezhi He Li Yang Yanzhi Wang Liqiang Wang Deliang Fan AAML 32 21 0 30 May 2019
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers Igor Fedorov Ryan P. Adams Matthew Mattina P. Whatmough 13 164 0 28 May 2019
Walsh-Hadamard Variational Inference for Bayesian Deep Learning Simone Rossi Sébastien Marmin Maurizio Filippone BDL 19 14 0 27 May 2019
Training CNNs with Selective Allocation of Channels Jongheon Jeong Jinwoo Shin CVBM 31 15 0 11 May 2019
Survey of Dropout Methods for Deep Neural Networks Alex Labach Hojjat Salehinejad S. Valaee 11 149 0 25 Apr 2019
How Can We Be So Dense? The Benefits of Using Highly Sparse Representations Subutai Ahmad Luiz Scheinkman 11 96 0 27 Mar 2019
Knowing what you know in brain segmentation using Bayesian deep neural networks Patrick McClure Nao Rho J. Lee Jakub R. Kaczmarzyk C. Zheng Satrajit S. Ghosh D. Nielson Adam G. Thomas P. Bandettini Francisco Pereira UQCV 3DV BDL 11 52 0 03 Dec 2018
Structured Pruning of Neural Networks with Budget-Aware Regularization Carl Lemaire Andrew Achkar Pierre-Marc Jodoin 19 92 0 23 Nov 2018
Metropolis-Hastings view on variational inference and adversarial training Kirill Neklyudov Evgenii Egorov Pavel Shvechikov Dmitry Vetrov GAN 13 13 0 16 Oct 2018
Rethinking the Value of Network Pruning Zhuang Liu Mingjie Sun Tinghui Zhou Gao Huang Trevor Darrell 8 1,449 0 11 Oct 2018
Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu Sebastian Nowozin Edward Meeds Richard E. Turner José Miguel Hernández-Lobato Alexander L. Gaunt UQCV AAML BDL 29 16 0 09 Oct 2018
Extracting representations of cognition across neuroimaging studies improves brain decoding A. Mensch Julien Mairal B. Thirion Gaël Varoquaux AI4CE 6 15 0 17 Sep 2018
A Survey on Methods and Theories of Quantized Neural Networks Yunhui Guo MQ 27 230 0 13 Aug 2018
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization Yibo Yang Nicholas Ruozzi Vibhav Gogate 11 2 0 14 Jun 2018
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors S. Ghosh Jiayu Yao Finale Doshi-Velez BDL UQCV 15 77 0 13 Jun 2018
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy Lance M. Kaplan M. Kandemir OOD UQCV EDL BDL 29 951 0 05 Jun 2018
Nonparametric Bayesian Deep Networks with Local Competition Konstantinos P. Panousis S. Chatzis Sergios Theodoridis BDL 9 32 0 19 May 2018
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning J. A. G. Higuera D. Meger Gregory Dudek BDL 17 39 0 06 Mar 2018
Compressing Neural Networks using the Variational Information Bottleneck Bin Dai Chen Zhu David Wipf MLT 20 178 0 28 Feb 2018
Bayesian Incremental Learning for Deep Neural Networks Max Kochurov T. Garipov D. Podoprikhin Dmitry Molchanov Arsenii Ashukha Dmitry Vetrov OOD CLL BDL 10 22 0 20 Feb 2018
Uncertainty Estimation via Stochastic Batch Normalization Andrei Atanov Arsenii Ashukha Dmitry Molchanov Kirill Neklyudov Dmitry Vetrov UQCV BDL 24 47 0 13 Feb 2018
Bayesian Compression for Deep Learning Christos Louizos Karen Ullrich Max Welling UQCV BDL 15 479 0 24 May 2017
Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton Nitish Srivastava A. Krizhevsky Ilya Sutskever Ruslan Salakhutdinov VLM 254 7,634 0 03 Jul 2012