Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima

30 April 2020

Papers citing "Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima"

21 / 21 papers shown

Title
LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks Enzo Tartaglione Andrea Bragagnolo Attilio Fiandrotti Marco Grangetto ODL UQCV 70 34 0 16 Nov 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 301 388 0 05 Mar 2020
Post-synaptic potential regularization has potential Enzo Tartaglione Daniele Perlo Marco Grangetto BDL AAML 48 6 0 19 Jul 2019
Learning Sparse Neural Networks via Sensitivity-Driven Regularization Enzo Tartaglione S. Lepsøy Attilio Fiandrotti Gianluca Francini 53 71 0 28 Oct 2018
Rethinking the Value of Network Pruning Zhuang Liu Mingjie Sun Tinghui Zhou Gao Huang Trevor Darrell 40 1,475 0 11 Oct 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle Michael Carbin 274 3,488 0 09 Mar 2018
Essentially No Barriers in Neural Network Energy Landscape Felix Dräxler K. Veschgini M. Salmhofer Fred Hamprecht MoMe 122 435 0 02 Mar 2018
Learning Sparse Neural Networks through $L_0$ Regularization Christos Louizos Max Welling Diederik P. Kingma 439 1,148 0 04 Dec 2017
To prune, or not to prune: exploring the efficacy of pruning for model compression Michael Zhu Suyog Gupta 202 1,282 0 05 Oct 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 285 8,928 0 25 Aug 2017
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression Jian-Hao Luo Jianxin Wu Weiyao Lin 58 1,761 0 20 Jul 2017
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 1.2K 20,900 0 17 Apr 2017
Soft Weight-Sharing for Neural Network Compression Karen Ullrich Edward Meeds Max Welling 172 419 0 13 Feb 2017
Variational Dropout Sparsifies Deep Neural Networks Dmitry Molchanov Arsenii Ashukha Dmitry Vetrov BDL 178 831 0 19 Jan 2017
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys Pratik Chaudhari A. Choromańska Stefano Soatto Yann LeCun Carlo Baldassi C. Borgs J. Chayes Levent Sagun R. Zecchina ODL 96 775 0 06 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 436 2,946 0 15 Sep 2016
Pruning Filters for Efficient ConvNets Hao Li Asim Kadav Igor Durdanovic H. Samet H. Graf 3DPC 195 3,707 0 31 Aug 2016
Learning both Weights and Connections for Efficient Neural Networks Song Han Jeff Pool J. Tran W. Dally CVBM 316 6,709 0 08 Jun 2015
Qualitatively characterizing neural network optimization problems Ian Goodfellow Oriol Vinyals Andrew M. Saxe ODL 114 524 0 19 Dec 2014
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation Emily L. Denton Wojciech Zaremba Joan Bruna Yann LeCun Rob Fergus FAtt 181 1,693 0 02 Apr 2014
Do Deep Nets Really Need to be Deep? Lei Jimmy Ba R. Caruana 188 2,120 0 21 Dec 2013