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Training convolutional neural network models is memory intensive since back-propagation requires storing activations of all intermediate layers.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1985
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Training deep and recurrent networks with hessian-free optimization
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1-bit Stochastic Gradient Descent and its Application to Data-parallel Distributed Training of Speech DNNs
Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu · 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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Song Han, Huizi Mao, and William J Dally · 2015
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The reversible residual network: Backpropagation without storing activations
Aidan N. Gomez, Mengye Ren, Raquel Urtasun, and Roger B. Grosse · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2017
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Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaev, Ganesh Venkatesh, et al · 2017
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Terngrad: Ternary gradients to reduce communication in distributed deep learning
Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2017
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