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Spending Your Winning Lottery Better After Drawing It

8 January 2021

Papers citing "Spending Your Winning Lottery Better After Drawing It"

20 / 20 papers shown

Title
GANs Can Play Lottery Tickets Too Xuxi Chen Zhenyu Zhang Yongduo Sui Tianlong Chen GAN 76 58 0 31 May 2021
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study Zhiqiang Shen Zechun Liu Dejia Xu Zitian Chen Kwang-Ting Cheng Marios Savvides 70 76 0 01 Apr 2021
RepVGG: Making VGG-style ConvNets Great Again Xiaohan Ding Xinming Zhang Ningning Ma Jungong Han Guiguang Ding Jian Sun 304 1,612 0 11 Jan 2021
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets Xiaohan Chen Yu Cheng Shuohang Wang Zhe Gan Zhangyang Wang Jingjing Liu 110 100 0 31 Dec 2020
The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models Tianlong Chen Jonathan Frankle Shiyu Chang Sijia Liu Yang Zhang Michael Carbin Zhangyang Wang 84 123 0 12 Dec 2020
Comparing Rewinding and Fine-tuning in Neural Network Pruning Alex Renda Jonathan Frankle Michael Carbin 304 388 0 05 Mar 2020
PyHessian: Neural Networks Through the Lens of the Hessian Z. Yao A. Gholami Kurt Keutzer Michael W. Mahoney ODL 89 304 0 16 Dec 2019
Winning the Lottery with Continuous Sparsification Pedro H. P. Savarese Hugo Silva Michael Maire 89 137 0 10 Dec 2019
Mish: A Self Regularized Non-Monotonic Activation Function Diganta Misra 102 680 0 23 Aug 2019
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP Haonan Yu Sergey Edunov Yuandong Tian Ari S. Morcos 58 150 0 06 Jun 2019
When Does Label Smoothing Help? Rafael Müller Simon Kornblith Geoffrey E. Hinton UQCV 214 1,958 0 06 Jun 2019
The State of Sparsity in Deep Neural Networks Trevor Gale Erich Elsen Sara Hooker 167 763 0 25 Feb 2019
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem Matthias Hein Maksym Andriushchenko Julian Bitterwolf OODD 182 559 0 13 Dec 2018
Rethinking the Value of Network Pruning Zhuang Liu Mingjie Sun Tinghui Zhou Gao Huang Trevor Darrell 42 1,477 0 11 Oct 2018
Averaging Weights Leads to Wider Optima and Better Generalization Pavel Izmailov Dmitrii Podoprikhin T. Garipov Dmitry Vetrov A. Wilson FedML MoMe 147 1,673 0 14 Mar 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle Michael Carbin 293 3,489 0 09 Mar 2018
Visualizing the Loss Landscape of Neural Nets Hao Li Zheng Xu Gavin Taylor Christoph Studer Tom Goldstein 270 1,901 0 28 Dec 2017
Pruning Filters for Efficient ConvNets Hao Li Asim Kadav Igor Durdanovic H. Samet H. Graf 3DPC 202 3,707 0 31 Aug 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 263 8,864 0 01 Oct 2015
Learning both Weights and Connections for Efficient Neural Networks Song Han Jeff Pool J. Tran W. Dally CVBM 323 6,715 0 08 Jun 2015