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Structured weight pruning is a representative model compression technique of DNNs for hardware efficiency and inference accelerations.
Distributed optimization and statistical learning via the alternating direction method of multipliers
Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein · 2011
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, et al · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Proximal algorithms
Neal Parikh and Stephen Boyd · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan 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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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Convergence analysis of alternating direction method of multipliers for a family of nonconvex problems
Mingyi Hong, Zhi-Quan Luo, and Meisam Razaviyayn · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
Cited alongside, same era.
Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, , Yandan Wang, Yiran Chen, and Hai Li · 2016
Cited alongside, same era.
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, et al · 2016
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Large-scale evolution of image classifiers
Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc V. Le, and Alexey Kurakin · 2017
Cited alongside, same era.
Neural architecture search with reinforcement learning
Barret Zoph and Quoc V. Le · 2017
Cited alongside, same era.
Systematic weight pruning of dnns using alternating direction method of multipliers
Tianyun Zhang, Shaokai Ye, Kaiqi Zhang, Jian Tang, Wujie Wen, Makan Fardad, and Yanzhi Wang · 2018
Later among the works it cites.
Adam-admm: A unified, systematic framework of structured weight pruning for dnns
Tianyun Zhang, Kaiqi Zhang, Shaokai Ye, Jian Tang, Wujie Wen, Xue Lin, Makan Fardad, and Yanzhi Wang · 2018
Later among the works it cites.
Improving deep neural network sparsity through decorrelation regularization
Xiaotian Zhu, Wengang Zhou, and Houqiang Li · 2018
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Discrimination-aware channel pruning for deep neural networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhang, et al · 2018
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Admm-nn: an algorithm-hardware co-design framework of dnns using alternating direction methods of multipliers
Ao Ren, Tianyun Zhang, Shaokai Ye, Wenyao Xu, Xuehai Qian, Xue Lin, and Yanzhi Wang · 2019
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N2n learning: Network to network compression via policy gradient reinforcement learning
Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, et al · 2018
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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
Cited alongside, same era.
Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2018
Cited alongside, same era.
2pfpce: Two-phase filter pruning based on conditional entropy
Chuhan Min, Aosen Wang, Yiran Chen, Wenyao Xu, and Xin Chen · 2018
Cited alongside, same era.
Non-structured dnn weight pruning considered harmful
Yanzhi Wang, Shaokai Ye, Zhezhi He, Xiaolong Ma, et al · 2019
Later among the works it cites.
Variational convolutional neural network pruning
Chenglong Zhao, Bingbing Ni, Jian Zhang, Qiwei Zhao, Wenjun Zhang, and Qi Tian · 2019
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Autocompress: An automatic dnn structured pruning framework for ultra-high compression rates
Ning Liu, Xiaolong Ma, Zhiyuan Xu, Yanzhi Wang, Jian Tang, and Jieping Ye · 2020
Closest in time.
Tiny but accurate: A pruned, quantized and optimized memristor crossbar framework for ultra efficient dnn implementation
Xiaolong Ma, Geng Yuan, Sheng Lin, Caiwen Ding, Fuxun Yu, Tao Liu, Wujie Wen, Xiang Chen, and Yanzhi Wang · 2020
Closest in time.