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Practitioners prune neural networks for efficiency gains and generalization improvements, but few scrutinize the factors determining the prunability of a neural network the maximum fraction of weights that pruning can remove without compromising the model's test accuracy.
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 1902
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Optimal brain damage
Yann LeCun, John Denker, and Sara Solla · 1989
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Optimal brain surgeon and general network pruning
B. Hassibi, D.G. Stork, and G.J. Wolff · 1993
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A kernel view of the dimensionality reduction of manifolds
Jihun Ham, Daniel D. Lee, Sebastian Mika, and Bernhard Schölkopf · 2004
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Algorithms for manifold learning
Lawrence Cayton · 2005
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On the sample complexity of learning smooth cuts on a manifold
Hariharan Narayanan and Partha Niyogi · 2009
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Sample complexity of testing the manifold hypothesis
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Monte carlo error analyses of spearman’s rank test
Peter A Curran · 2014
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Learning both weights and connections for efficient neural network
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Testing the manifold hypothesis
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Fast convnets using group-wise brain damage
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Pruning convolutional neural networks for resource efficient inference
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Stronger generalization bounds for deep nets via a compression approach
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Large scale gan training for high fidelity natural image synthesis
SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip Torr · 2019
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EigenDamage: Structured pruning in the Kronecker-factored eigenbasis
Chaoqi Wang, Roger Grosse, Sanja Fidler, and Guodong Zhang · 2019
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What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 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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A signal propagation perspective for pruning neural networks at initialization
Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, and Philip H. S. Torr · 2020
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Comparing rewinding and fine-tuning in neural network pruning
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Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Nisp: Pruning networks using neuron importance score propagation
Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, and Larry S Davis · 2018
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Data-dependent coresets for compressing neural networks with applications to generalization bounds
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, and Daniela Rus · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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Lossless compression of deep neural networks
Thiago Serra, Abhinav Kumar, and Srikumar Ramalingam · 2020
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The intrinsic dimension of images and its impact on learning
Phil Pope, Chen Zhu, Ahmed Abdelkader, Micah Goldblum, and Tom Goldstein · 2021
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On the predictability of pruning across scales
Jonathan S Rosenfeld, Jonathan Frankle, Michael Carbin, and Nir Shavit · 2021
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