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Recent advances in the sparse neural network literature have made it possible to prune many large feed forward and convolutional networks with only a small quantity of data.
A back-propagation algorithm with optimal use of hidden units
Yves Chauvin · 1989
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Simplifying neural networks by soft weight-sharing
Steven J Nowlan and Geoffrey E Hinton · 1992
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Optimal brain surgeon and general network pruning
Babak Hassibi, David G Stork, and Gregory J Wolff · 1993
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Pruning algorithms-a survey
Russell Reed · 1993
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Structural learning with forgetting
Masumi Ishikawa · 1996
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson · 2013
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On the difficulty of training recurrent neural networks
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio · 2013
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Compressing deep convolutional networks using vector quantization
Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
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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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Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Dynamic network surgery for efficient dnns
Yiwen Guo, Anbang Yao, and Yurong Chen · 2016
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Compression of neural machine translation models via pruning
Abigail See, Minh-Thang Luong, and Christopher D Manning · 2016
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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
Pruning neural networks: is it time to nip it in the bud?
Elliot J Crowley, Jack Turner, Amos Storkey, and Michael O’Boyle · 2018
Later among the works it cites.
Grow and prune compact, fast, and accurate lstms
Xiaoliang Dai, Hongxu Yin, and Niraj K Jha · 2018
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Mean replacement pruning
Utku Evci, Nicolas Le Roux, Pablo Castro, and Leon Bottou · 2018
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Snip: Single-shot network pruning based on connection sensitivity
Namhoon Lee, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Rethinking the value of network pruning
Zhuang Liu, Mingjie Sun, Tinghui Zhou, Gao Huang, and Trevor Darrell · 2018
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Trace norm regularization and faster inference for embedded speech recognition rnns
Markus Kliegl, Siddharth Goyal, Kexin Zhao, Kavya Srinet, and Mohammad Shoeybi · 2017
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Bayesian sparsification of recurrent neural networks
Ekaterina Lobacheva, Nadezhda Chirkova, and Dmitry Vetrov · 2017
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Variational dropout sparsifies deep neural networks
Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov · 2017
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Soft weight-sharing for neural network compression
Karen Ullrich, Edward Meeds, and Max Welling · 2017
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Learning intrinsic sparse structures within long short-term memory
Wei Wen, Yuxiong He, Samyam Rajbhandari, Minjia Zhang, Wenhan Wang, Fang Liu, Bin Hu, Yiran Chen, and Hai Li · 2017
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Stronger generalization bounds for deep nets via a compression approach
Sanjeev Arora, Rong Ge, Behnam Neyshabur, and Yi Zhang · 2018
Cited alongside, same era.
“learning-compression” algorithms for neural net pruning
Miguel A Carreira-Perpinán and Yerlan Idelbayev · 2018
Cited alongside, same era.
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Hardware-oriented compression of long short-term memory for efficient inference
Zhisheng Wang, Jun Lin, and Zhongfeng Wang · 2018
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Learning compact recurrent neural networks with block-term tensor decomposition
Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, and Zenglin Xu · 2018
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Foresight pruning
A. Anonymous · 2019
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
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The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
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Dynamical isometry and a mean field theory of lstms and grus
Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S Schoenholz, Ed H Chi, and Jeffrey Pennington · 2019
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