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Weight pruning methods of DNNs have been demonstrated to achieve a good model pruning rate without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs.
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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On the linear convergence of the alternating direction method of multipliers
Mingyi Hong and Zhi-Quan Luo · 2017
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Exploring the regularity of sparse structure in convolutional neural networks
Huizi Mao, Song Han, Jeff Pool, Wenshuo Li, Xingyu Liu, Yu Wang, and William J Dally · 2017
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Wei Wen, Cong Xu, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2017
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Theoretical properties for neural networks with weight matrices of low displacement rank
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Zeroth-order online alternating direction method of multipliers: Convergence analysis and applications
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A systematic dnn weight pruning framework using alternating direction method of multipliers
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