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Previous work showed empirically that large neural networks can be significantly reduced in size while preserving their accuracy.
Skeletonization: A technique for trimming the fat from a network via relevance assessment
Michael C Mozer and Paul Smolensky · 1989
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Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, Patrick Haffner, et al · 1998
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Neural network learning: Theoretical foundations
Martin Anthony and Peter L Bartlett · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
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Universal epsilon-approximators for integrals
Michael Langberg and Leonard J. Schulman · 2010
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A note on element-wise matrix sparsification via a matrix-valued bernstein inequality
Petros Drineas and Anastasios Zouzias · 2011
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A unified framework for approximating and clustering data
Dan Feldman and Michael Langberg · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Matrix entry-wise sampling: Simple is best, 2013
Dimitris Achlioptas, Zohar Karnin, and Edo Liberty · 2013
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
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A note on randomized element-wise matrix sparsification
Abhisek Kundu and Petros Drineas · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 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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On the uniform convergence of relative frequencies of events to their probabilities
Vladimir N Vapnik and A Ya Chervonenkis · 2015
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New frameworks for offline and streaming coreset constructions
Vladimir Braverman, Dan Feldman, and Harry Lang · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2017
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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Pytorch: Tensors and dynamic neural networks in python with strong gpu acceleration
Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan · 2017
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Data-dependent coresets for compressing neural networks with applications to generalization bounds
Cenk Baykal, Lucas Liebenwein, Igor Gilitschenski, Dan Feldman, and Daniela Rus · 2018
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Coreset-based neural network compression
Abhimanyu Dubey, Moitreya Chatterjee, and Narendra Ahuja · 2018
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Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J. Dally · 2016
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Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang · 2016
Cited alongside, same era.
Fast convnets using group-wise brain damage
Vadim Lebedev and Victor Lempitsky · 2016
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Jeff M Phillips · 2016
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Compression-aware training of deep networks
Jose M Alvarez and Mathieu Salzmann · 2017
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Learning to prune deep neural networks via layer-wise optimal brain surgeon
Xin Dong, Shangyu Chen, and Sinno Pan · 2017
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Channel pruning for accelerating very deep neural networks
Yihui He, Xiangyu Zhang, and Jian Sun · 2017
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew G. Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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Towards understanding the role of over-parametrization in generalization of neural networks
Behnam Neyshabur, Zhiyuan Li, Srinadh Bhojanapalli, Yann LeCun, and Nathan Srebro · 2018
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Coresets for monotonic functions with applications to deep learning
Elad Tolochinsky and Dan Feldman · 2018
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NISP: pruning networks using neuron importance score propagation
Ruichi Yu, Ang Li, Chun-Fu Chen, Jui-Hsin Lai, Vlad I. Morariu, Xintong Han, Mingfei Gao, Ching-Yung Lin, and Larry S. Davis · 2018
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Discrimination-aware channel pruning for deep neural networks
Zhuangwei Zhuang, Mingkui Tan, Bohan Zhuang, Jing Liu, Yong Guo, Qingyao Wu, Junzhou Huang, and Jinhui Zhu · 2018
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A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 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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