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Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model.
Optimal brain damage
Yann LeCun, John S. Denker, and Sara A. Solla · 1990
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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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Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan V. Oseledets, and Victor S. Lempitsky · 2014
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Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
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Structured pruning of deep convolutional neural networks
Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung · 2015
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Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Fast convnets using group-wise brain damage
Vadim Lebedev and Victor S. Lempitsky · 2015
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Neural networks with few multiplications
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio · 2015
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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
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Cambricon-x: An accelerator for sparse neural networks
Shijin Zhang, Zidong Du, Lei Zhang, Huiying Lan, Shaoli Liu, Ling Li, Qi Guo, Tianshi Chen, and Yunji Chen · 2016
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J. Dally · 2016
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EIE: efficient inference engine on compressed deep neural network
Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, and William J. Dally · 2016
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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 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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Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Compression of neural machine translation models via pruning
Abigail See, Minh-Thang Luong, and Christopher D. Manning · 2016
Cited alongside, same era.
Song Han, Huizi Mao, and William J. Dally
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally
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