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Pruning the parameters of deep neural networks has generated intense interest due to potential savings in time, memory and energy both during training and at test time.
Linear mode connectivity and the lottery ticket hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin · 1912
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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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Pruning versus clipping in neural networks
Steven A Janowsky · 1989
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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A simple procedure for pruning back-propagation trained neural networks
Ehud D Karnin · 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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Pruning algorithms-a survey
Russell Reed · 1993
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Speeding up convolutional neural networks with low rank expansions
Max Jaderberg, Andrea Vedaldi, and Andrew Zisserman · 2014
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Tensorizing neural networks
Alexander Novikov, Dmitrii Podoprikhin, Anton Osokin, and Dmitry P Vetrov · 2015
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Path-sgd: Path-normalized optimization in deep neural networks
Behnam Neyshabur, Russ R Salakhutdinov, and Nati Srebro · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Efficient processing of deep neural networks: A tutorial and survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel S Emer · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
Cited alongside, same era.
Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Pan · 2017
Cited alongside, same era.
L2 regularization versus batch and weight normalization
Twan Van Laarhoven · 2017
Cited alongside, same era.
Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
Cited alongside, same era.
Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2019
Later among the works it cites.
MLPrune: Multi-layer pruning for automated neural network compression, 2019
Wenyuan Zeng and Raquel Urtasun · 2019
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Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
Hesham Mostafa and Xin Wang · 2019
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Fisher-rao metric, geometry, and complexity of neural networks
Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, and James Stokes · 2019
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How important is a neuron
Kedar Dhamdhere, Mukund Sundararajan, and Qiqi Yan · 2019
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Pruning by explaining: A novel criterion for deep neural network pruning
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Deep expander networks: Efficient deep networks from graph theory
Ameya Prabhu, Girish Varma, and Anoop Namboodiri · 2018
Cited alongside, same era.
Deep rewiring: Training very sparse deep networks
Guillaume Bellec, David Kappel, Wolfgang Maass, and Robert Legenstein · 2018
Cited alongside, same era.
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced
Simon S Du, Wei Hu, and Jason D Lee · 2018
Cited alongside, same era.
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, and Surya Ganguli · 2019
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
Later among the works it cites.
Deconstructing lottery tickets: Zeros, signs, and the supermask
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2019
Later among the works it cites.
Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 2020
Closest in time.
What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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Lookahead: A far-sighted alternative of magnitude-based pruning
Sejun Park*, Jaeho Lee*, Sangwoo Mo, and Jinwoo Shin · 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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Pruning untrained neural networks: Principles and analysis
Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, and Yee Whye Teh · 2020
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Drawing early-bird tickets: Toward more efficient training of deep networks
Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, and Yingyan Lin · 2020
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Playing the lottery with rewards and multiple languages: lottery tickets in rl and nlp
Haonan Yu, Sergey Edunov, Yuandong Tian, and Ari S. Morcos · 2020
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Pruning via iterative ranking of sensitivity statistics
Stijn Verdenius, Maarten Stol, and Patrick Forré · 2020
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Progressive skeletonization: Trimming more fat from a network at initialization
Pau de Jorge, Amartya Sanyal, Harkirat S Behl, Philip HS Torr, Gregory Rogez, and Puneet K Dokania · 2020
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Pruning neural networks at initialization: Why are we missing the mark?
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin · 2020
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