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The strong {\it lottery ticket hypothesis} (LTH) postulates that one can approximate any target neural network by only pruning the weights of a sufficiently over-parameterized random network.
Reducibility among Combinatorial Problems
Richard M. Karp · 1972
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On the Average Difference between the Solutions to Linear and Integer Knapsack Problems
George S. Lueker · 1982
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Probabilistic Analysis of Optimum Partitioning
Narendra Karmarkar, Richard M. Karp, George S. Lueker, and Andrew M. Odlyzko · 1986
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Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment
Michael C Mozer and Paul Smolensky · 1989
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Optimal Brain Damage
Yann LeCun, John S. Denker, and Sara A. Solla · 1990
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Second order derivatives for network pruning: Optimal Brain Surgeon
Babak Hassibi and David G. Stork · 1993
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Fast Pruning Using Principal Components
Asriel U. Levin, Todd K. Leen, and John E. Moody · 1994
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Exponentially small bounds on the expected optimum of the partition and subset sum problems
George S. Lueker · 1998
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Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Learning both weights and connections for efficient neural network
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Song Han, Huizi Mao, and William J. Dally · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Binarized neural networks
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Quantized convolutional neural networks for mobile devices
Jiaxiang Wu, Cong Leng, Yuhang Wang, Qinghao Hu, and Jian Cheng · 2016
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
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SGDR: stochastic gradient descent with restarts
Ilya Loshchilov and Frank Hutter · 2016
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Using winning lottery tickets in transfer learning for convolutional neural networks
R. V. Soelen and J. W. Sheppard · 2019
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Yulong Wang, Xiaolu Zhang, Lingxi Xie, Jun Zhou, Hang Su, Bo Zhang, and Xiaolin Hu · 2019
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Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
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Proving the Lottery Ticket Hypothesis: Pruning is All You Need
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, and Ohad Shamir · 2020
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Model compression and hardware acceleration for neural networks: A comprehensive survey
Lei Deng, Guoqi Li, Song Han, Luping Shi, and Yuan Xie · 2020
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What is the state of neural network pruning?
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Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
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Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
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The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
Jonathan Frankle and Michael Carbin · 2018
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High-Dimensional Probability: An Introduction with Applications in Data Science
Roman Vershynin · 2018
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A Survey of Model Compression and Acceleration for Deep Neural Networks
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang · 2019
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Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2020
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Linear Mode Connectivity and the Lottery Ticket Hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin · 2020
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On the transferability of winning tickets in non-natural image datasets
Matthia Sabatelli, Mike Kestemont, and Pierre Geurts · 2020
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What’s Hidden in a Randomly Weighted Neural Network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
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Logarithmic pruning is all you need, 2020
Laurent Orseau, Marcus Hutter, and Omar Rivasplata · 2020
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Gurobi optimizer reference manual, 2020
LLC Gurobi Optimization · 2020
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