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Fully exploiting the learning capacity of neural networks requires overparameterized dense networks.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Second order derivatives for network pruning: Optimal brain surgeon
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Imagenet: A large-scale hierarchical image database
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Neural pruning via growing regularization
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Pruning filters for efficient convnets
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Suraj Srinivas and R Venkatesh Babu · 2016
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Deep rewiring: Training very sparse deep networks
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A survey of model compression and acceleration for deep neural networks
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang · 2017
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Channel pruning for accelerating very deep neural networks
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Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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Learning sparse neural networks through l _ 0 l\_0 regularization
Christos Louizos, Max Welling, and Diederik P Kingma · 2017
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The state of sparsity in deep neural networks
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A signal propagation perspective for pruning neural networks at initialization
Namhoon Lee, Thalaiyasingam Ajanthan, Stephen Gould, and Philip HS Torr · 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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Fast convergence of natural gradient descent for overparameterized neural networks
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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
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Jianbo Ye, Xin Lu, Zhe Lin, and James Z Wang · 2018
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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
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Song Han, Huizi Mao, and William J Dally
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Learning both weights and connections for efficient neural networks
Song Han, Jeff Pool, John Tran, and William J Dally
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Guodong Zhang, James Martens, and Roger Grosse · 2019
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Model compression and hardware acceleration for neural networks: A comprehensive survey
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Rigging the lottery: Making all tickets winners
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Woodfisher: Efficient second-order approximations for model compression
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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, Yingyan Lin, Zhangyang Wang, and Richard G. Baraniuk · 2020
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Emerging paradigms of neural network pruning
Huan Wang, Can Qin, Yulun Zhang, and Yun Fu · 2021
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