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Network pruning is a method for reducing test-time computational resource requirements with minimal performance degradation.
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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Optimal brain surgeon and general network pruning
Babak Hassibi, David G.Stork, and Gregory Wolff · 1993
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Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David G Stork · 1993
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Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2009
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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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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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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Learning the number of neurons in deep networks
Jose M Alvarez and Mathieu Salzmann · 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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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
Earlier work this paper cites.
Suraj Srinivas and R Venkatesh Babu · 2016
Earlier work this paper cites.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc Le · 2016
Earlier work this paper cites.
Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Jialin Pan · 2017
Cited alongside, same era.
Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
Cited alongside, same era.
Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
Cited alongside, same era.
Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
Cited alongside, same era.
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
Cited alongside, same era.
“learning-compression” algorithms for neural net pruning
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
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SNIP: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip Torr · 2019
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DARTS: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2019
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Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2019
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One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
Ari Morcos, Haonan Yu, Michela Paganini, and Yuandong Tian · 2019
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Deconstructing lottery tickets: Zeros, signs, and the supermask
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Miguel A Carreira-Perpinán and Yerlan Idelbayev · 2018
Cited alongside, same era.
Data-driven sparse structure selection for deep neural networks
Zehao Huang and Naiyan Wang · 2018
Cited alongside, same era.
Learning sparse neural networks through l 0 l_{0} regularization
Christos Louizos, Max Welling, and Diederik P. Kingma · 2018
Cited alongside, same era.
Towards understanding the role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann Lecun, and Nathan Srebro · 2018
Cited alongside, same era.
Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
Cited alongside, same era.
Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks
Sanjeev Arora, Simon Du, Wei Hu, Zhiyuan Li, and Ruosong Wang · 2019
Cited alongside, same era.
ProxylessNAS: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
Cited alongside, same era.
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2019
Later among the works it cites.
What is the state of neural network pruning?
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
Closest in time.
Pruning neural networks at initialization: Why are we missing the mark?
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M Roy, and Michael Carbin · 2020
Closest in time.
Pruning untrained neural networks: Principles and analysis
Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, and Yee Whye Teh · 2020
Closest in time.
Proving the lottery ticket hypothesis: Pruning is all you need
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz, and Ohad Shamir · 2020
Closest in time.
Data-independent neural pruning via coresets
Ben Mussay, Margarita Osadchy, Vladimir Braverman, Samson Zhou, and Dan Feldman · 2020
Closest in time.
Comparing rewinding and fine-tuning in neural network pruning
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
Closest in time.
Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel LK Yamins, and Surya Ganguli · 2020
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Picking winning tickets before training by preserving gradient flow
Chaoqi Wang, Guodong Zhang, and Roger Grosse · 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
Closest in time.