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In this paper, we propose a novel meta learning approach for automatic channel pruning of very deep neural networks.
Optimal brain damage
Yann LeCun, John S Denker, and Sara A Solla · 1990
Earlier work this paper cites.
Optimal brain surgeon and general network pruning
Babak Hassibi, David G Stork, and Gregory J Wolff · 1993
Earlier work this paper cites.
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Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
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Misha Denil, Babak Shakibi, Laurent Dinh, Nando De Freitas, et al · 2013
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Earlier work this paper cites.
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Earlier work this paper cites.
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