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Variational Dropout Sparsifies Deep Neural Networks

International Conference on Machine Learning (ICML), 2017

19 January 2017

Dmitry Vetrov

Papers citing "Variational Dropout Sparsifies Deep Neural Networks"

50 / 481 papers shown

Title
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference Kumar Shridhar F. Laumann Marcus Liwicki BDL UQCV 189 202 0 08 Jan 2019
GASL: Guided Attention for Sparsity Learning in Deep Neural Networks A. Torfi Rouzbeh A. Shirvani Sobhan Soleymani Nasser M. Nasrabadi 232 8 0 07 Jan 2019
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning Sirui Xie Junning Huang Lanxin Lei Chunxiao Liu Zheng Ma Wayne Zhang Liang Lin 107 9 0 21 Dec 2018
Entropy-Constrained Training of Deep Neural Networks Simon Wiedemann Arturo Marbán K. Müller Wojciech Samek 171 30 0 18 Dec 2018
Bayesian Sparsification of Gated Recurrent Neural Networks E. Lobacheva Nadezhda Chirkova Dmitry Vetrov BDL 89 2 0 12 Dec 2018
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network Yinghao Xu Xin Dong Yudian Li Hao Su 99 31 0 11 Dec 2018
Channel selection using Gumbel Softmax Charles Herrmann Richard Strong Bowen Ramin Zabih 93 3 0 11 Dec 2018
Efficient and Robust Machine Learning for Real-World Systems Franz Pernkopf Wolfgang Roth Matthias Zöhrer Lukas Pfeifenberger Günther Schindler Holger Froening Sebastian Tschiatschek Robert Peharz Matthew Mattina Zoubin Ghahramani OOD 99 1 0 05 Dec 2018
Stochastic Model Pruning via Weight Dropping Away and Back Haipeng Jia Xueshuang Xiang Da Fan Meiyu Huang Changhao Sun Yang He 148 3 0 05 Dec 2018
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 196 56 0 03 Dec 2018
Accelerate CNN via Recursive Bayesian Pruning Yuefu Zhou Ya Zhang Yanfeng Wang Qi Tian BDL 197 62 0 02 Dec 2018
Leveraging Filter Correlations for Deep Model Compression Pravendra Singh Vinay Kumar Verma Piyush Rai Vinay P. Namboodiri 205 74 0 26 Nov 2018
Structured Pruning of Neural Networks with Budget-Aware RegularizationComputer Vision and Pattern Recognition (CVPR), 2018 Carl Lemaire Andrew Achkar Pierre-Marc Jodoin 209 95 0 23 Nov 2018
Structured Pruning for Efficient ConvNets via Incremental RegularizationIEEE International Joint Conference on Neural Network (IJCNN), 2018 Huan Wang Qiming Zhang Yuehai Wang Haoji Hu 3DPC 239 49 0 20 Nov 2018
Variational Bayesian Dropout with a Hierarchical PriorComputer Vision and Pattern Recognition (CVPR), 2018 Yuhang Liu Wenyong Dong Lei Zhang Dong Gong Javen Qinfeng Shi BDL 147 20 0 19 Nov 2018
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation Methods Caroline Werther M. Ferguson K. Park Cuixian Chen Stephen J. Roberts ODL 146 4 0 08 Nov 2018
Analysing Dropout and Compounding Errors in Neural Language Models James OÑeill Danushka Bollegala 76 1 0 02 Nov 2018
Variational Dropout via Empirical Bayes V. Kharitonov Dmitry Molchanov Dmitry Vetrov BDL 154 10 0 01 Nov 2018
DeepTwist: Learning Model Compression via Occasional Weight Distortion Dongsoo Lee Parichay Kapoor Byeongwook Kim 137 19 0 30 Oct 2018
Learning Sparse Neural Networks via Sensitivity-Driven Regularization Enzo Tartaglione S. Lepsøy Attilio Fiandrotti Gianluca Francini 78 71 0 28 Oct 2018
Attended Temperature Scaling: A Practical Approach for Calibrating Deep Neural Networks A. Mozafari H. Gomes Wilson Leão Steeven Janny Christian Gagné 219 32 0 27 Oct 2018
Bayesian Compression for Natural Language Processing Nadezhda Chirkova E. Lobacheva Dmitry Vetrov BDL 129 15 0 25 Oct 2018
Distilling with Performance Enhanced Students Jack Turner Elliot J. Crowley Valentin Radu José Cano Amos Storkey Michael F. P. O'Boyle 132 4 0 24 Oct 2018
Sparse DNNs with Improved Adversarial Robustness Yiwen Guo Chao Zhang Changshui Zhang Yurong Chen AAML 176 163 0 23 Oct 2018
Metropolis-Hastings view on variational inference and adversarial training Kirill Neklyudov Evgenii Egorov Pavel Shvechikov Dmitry Vetrov GAN 129 13 0 16 Oct 2018
The Deep Weight Prior Andrei Atanov Arsenii Ashukha Kirill Struminsky Dmitry Vetrov Max Welling BDL 189 37 0 16 Oct 2018
Rethinking the Value of Network Pruning Zhuang Liu Mingjie Sun Tinghui Zhou Gao Huang Trevor Darrell 277 1,599 0 11 Oct 2018
Understanding Priors in Bayesian Neural Networks at the Unit Level M. Vladimirova Jakob Verbeek Pablo Mesejo Julyan Arbel BDL UQCV 225 4 0 11 Oct 2018
A Closer Look at Structured Pruning for Neural Network Compression Elliot J. Crowley Jack Turner Amos Storkey Michael F. P. O'Boyle 3DPC 157 31 0 10 Oct 2018
Dropout as a Structured Shrinkage Prior Eric T. Nalisnick José Miguel Hernández-Lobato Padhraic Smyth BDL UQCV 203 1 0 09 Oct 2018
Deterministic Variational Inference for Robust Bayesian Neural Networks Anqi Wu Sebastian Nowozin Edward Meeds Richard Turner José Miguel Hernández-Lobato Alexander L. Gaunt UQCV AAML BDL 214 16 0 09 Oct 2018
SNIP: Single-shot Network Pruning based on Connection Sensitivity Namhoon Lee Thalaiyasingam Ajanthan Juil Sock VLM 634 1,359 0 04 Oct 2018
Training Behavior of Sparse Neural Network Topologies Simon Alford Ryan A. Robinett Lauren Milechin J. Kepner 112 17 0 30 Sep 2018
NICE: Noise Injection and Clamping Estimation for Neural Network Quantization Chaim Baskin Natan Liss Yoav Chai Evgenii Zheltonozhskii Eli Schwartz Raja Giryes A. Mendelson A. Bronstein MQ 170 64 0 29 Sep 2018
Extracting representations of cognition across neuroimaging studies improves brain decoding A. Mensch Julien Mairal Bertrand Thirion Gaël Varoquaux AI4CE 245 16 0 17 Sep 2018
Learning Sparse Low-Precision Neural Networks With Learnable Regularization Yoojin Choi Mostafa El-Khamy Jungwon Lee MQ 252 31 0 01 Sep 2018
Predefined Sparseness in Recurrent Sequence Models T. Demeester Johannes Deleu Fréderic Godin Chris Develder 79 3 0 27 Aug 2018
Catastrophic Importance of Catastrophic Forgetting Albert Ierusalem CLL AI4CE 47 2 0 20 Aug 2018
A Survey on Methods and Theories of Quantized Neural Networks Yunhui Guo MQ 250 257 0 13 Aug 2018
Variational Bayesian dropout: pitfalls and fixesInternational Conference on Machine Learning (ICML), 2018 Jiri Hron A. G. Matthews Zoubin Ghahramani BDL 152 70 0 05 Jul 2018
Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference Kumar Shridhar F. Laumann Marcus Liwicki BDL UQCV 287 19 0 15 Jun 2018
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization Jianlong Wu Nicholas Ruozzi Vibhav Gogate 120 3 0 14 Jun 2018
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors S. Ghosh Jiayu Yao Finale Doshi-Velez BDL UQCV 151 81 0 13 Jun 2018
Full deep neural network training on a pruned weight budget Maximilian Golub G. Lemieux Mieszko Lis 210 30 0 11 Jun 2018
Evidential Deep Learning to Quantify Classification Uncertainty Murat Sensoy Lance M. Kaplan M. Kandemir OOD UQCV EDL BDL 619 1,240 0 05 Jun 2018
Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout Juho Lee Saehoon Kim Jaehong Yoon Haebeom Lee Eunho Yang Sung Ju Hwang 129 12 0 28 May 2018
Distributed Weight Consolidation: A Brain Segmentation Case Study Patrick McClure C. Zheng Jakub R. Kaczmarzyk John Rogers-Lee Satrajit S. Ghosh D. Nielson P. Bandettini Francisco Pereira 262 29 0 28 May 2018
Compact and Computationally Efficient Representation of Deep Neural Networks Simon Wiedemann K. Müller Wojciech Samek MQ 196 74 0 27 May 2018
Implicit Reparameterization Gradients Michael Figurnov S. Mohamed A. Mnih BDL 508 247 0 22 May 2018
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints Yoojin Choi Mostafa El-Khamy Jungwon Lee 143 6 0 21 May 2018