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Network sparsity receives popularity mostly due to its capability to reduce the network complexity.
Optimal brain damage
Yann LeCun, John Denker, and Sara Solla · 1989
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Skeletonization: A technique for trimming the fat from a network via relevance assessment
Michael C Mozer and Paul Smolensky · 1989
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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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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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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Thinet: A filter level pruning method for deep neural network compression
Jian-Hao Luo, Jianxin Wu, and Weiyao Lin · 2017
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Pruning convolutional neural networks for resource efficient inference
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
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Auto-balanced filter pruning for efficient convolutional neural networks
Xiaohan Ding, Guiguang Ding, Jungong Han, and Sheng Tang · 2018
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Soft filter pruning for accelerating deep convolutional neural networks
Yang He, Guoliang Kang, Xuanyi Dong, Yanwei Fu, and Yi Yang · 2018
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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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Sparse networks from scratch: Faster training without losing performance
Tim Dettmers and Luke Zettlemoyer · 2019
Cited alongside, same era.
Centripetal sgd for pruning very deep convolutional networks with complicated structure
Xiaohan Ding, Guiguang Ding, Yuchen Guo, and Jungong Han · 2019
Cited alongside, same era.
Global sparse momentum sgd for pruning very deep neural networks
Xiaohan Ding, Guiguang Ding, Xiangxin Zhou, Yuchen Guo, Jungong Han, and Ji Liu · 2019
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
Cited alongside, same era.
Sparse gpu kernels for deep learning
Trevor Gale, Matei Zaharia, Cliff Young, and Erich Elsen · 2020
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Learning filter pruning criteria for deep convolutional neural networks acceleration
Yang He, Yuhang Ding, Ping Liu, Linchao Zhu, Hanwang Zhang, and Yi Yang · 2020
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Soft threshold weight reparameterization for learnable sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, and Ali Farhadi · 2020
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Layer-adaptive sparsity for the magnitude-based pruning
Jaeho Lee, Sejun Park, Sangwoo Mo, Sungsoo Ahn, and Jinwoo Shin · 2020
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Hrank: Filter pruning using high-rank feature map
Mingbao Lin, Rongrong Ji, Yan Wang, Yichen Zhang, Baochang Zhang, Yonghong Tian, and Ling Shao · 2020
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Dynamic model pruning with feedback
Tao Lin, Sebastian U Stich, Luis Barba, Daniil Dmitriev, and Martin Jaggi · 2020
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Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang · 2019
Cited alongside, same era.
Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip Torr · 2019
Cited alongside, same era.
Structured pruning of neural networks with budget-aware regularization
Carl Lemaire, Andrew Achkar, and Pierre-Marc Jodoin · 2019
Cited alongside, same era.
Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2019
Cited alongside, same era.
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
Hesham Mostafa and Xin Wang · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Winning the lottery with continuous sparsification
Pedro Savarese, Hugo Silva, and Michael Maire · 2019
Cited alongside, same era.
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Logarithmic pruning is all you need
Laurent Orseau, Marcus Hutter, and Omar Rivasplata · 2020
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Optimal lottery tickets via subsetsum: Logarithmic over-parameterization is sufficient
Ankit Pensia, Shashank Rajput, Alliot Nagle, Harit Vishwakarma, and Dimitris Papailiopoulos · 2020
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What’s hidden in a randomly weighted neural network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
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Pruning neural networks without any data by iteratively conserving synaptic flow
Hidenori Tanaka, Daniel Kunin, Daniel L 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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Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, and Alexandra Peste · 2021
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Sparse training via boosting pruning plasticity with neuroregeneration
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, and Decebal Constantin Mocanu · 2021
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Do we actually need dense over-parameterization? in-time over-parameterization in sparse training
Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, and Mykola Pechenizkiy · 2021
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Lottery jackpots exist in pre-trained models
Yuxin Zhang, Mingbao Lin, Fei Chao, Yan Wang, Ke Li, Yunhang Shen, Yongjian Wu, and Rongrong Ji · 2021
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Learning n: M fine-grained structured sparse neural networks from scratch
Aojun Zhou, Yukun Ma, Junnan Zhu, Jianbo Liu, Zhijie Zhang, Kun Yuan, Wenxiu Sun, and Hongsheng Li · 2021
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Learning best combination for efficient n: M sparsity
Yuxin Zhang, Mingbao Lin, Zhihang Lin, Yiting Luo, Ke Li, Fei Chao, Yongjian Wu, and Rongrong Ji · 2022
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