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Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation.
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Perry Moerland and Emile Fiesler · 1997
Earlier work this paper cites.
Learning multiple layers of features from tiny images, 2009
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Earlier work this paper cites.
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Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
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Ian J. Goodfellow, David Warde-Farley, Pascal Lamblin, Vincent Dumoulin, Mehdi Mirza, Razvan Pascanu, James Bergstra, Frédéric Bastien, and Yoshua Bengio · 2013
Earlier work this paper cites.
Min Lin, Qiang Chen, and Shuicheng Yan · 2013
Earlier work this paper cites.
Regularization of neural networks using dropconnect
Li Wan, Matthew Zeiler, Sixin Zhang, Yann L Cun, and Rob Fergus · 2013
Earlier work this paper cites.
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Paul A Merolla, John V Arthur, Rodrigo Alvarez-Icaza, Andrew S Cassidy, Jun Sawada, Filipp Akopyan, Bryan L Jackson, Nabil Imam, Chen Guo, Yutaka Nakamura, et al · 2014
Earlier work this paper cites.
Training deep neural networks with low precision multiplications
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2014
Earlier work this paper cites.
Expectation backpropagation: parameter-free training of multilayer neural networks with continuous or discrete weights
Daniel Soudry, Itay Hubara, and Ron Meir · 2014
Earlier work this paper cites.
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Diederik Kingma and Jimmy Ba · 2014
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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