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Low precision weights, activations, and gradients have been proposed as a way to improve the computational efficiency and memory footprint of deep neural networks.
Gradient-based learning applied to document recognition
Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, et al · 2012
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Regularization of neural networks using dropconnect
L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J. David · 2015
Earlier work this paper cites.
An analysis of deep neural network models for practical applications
A. Canziani, A. Paszke, and E. Culurciello · 2016
Earlier work this paper cites.
Towards the limit of network quantization
Y. Choi, M. El-Khamy, and J. Lee · 2016
Earlier work this paper cites.
Binarynet: Training deep neural networks with weights and activations constrained to +1 or -1
M. Courbariaux and Y. Bengio · 2016
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Categorical reparameterization with Gumbel-Softmax
E. Jang, S. Gu, and B. Poole · 2016
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Training skinny deep neural networks with iterative hard thresholding methods
X. Jin, X. Yuan, J. Feng, and S. Yan · 2016
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M. Kim and P. Smaragdis · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
DoReFa-Net: Training low bitwidth convolutional neural networks with low bitwidth gradients
S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou · 2016
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C. Zhu, S. Han, H. Mao, and W. J. Dally · 2016
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Soft-to-hard vector quantization for end-to-end learned compression of images and neural networks
E. Agustsson, F. Mentzer, M. Tschannen, L. Cavigelli, R. Timofte, L. Benini, and L. Van Gool · 2017
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Structured pruning of deep convolutional neural networks
S. Anwar, K. Hwang, and W. Sung · 2017
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Deep learning with low precision by half-wave gaussian quantization
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C. J. Maddison, A. Mnih, and Y. W. Teh · 2016
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Deep neural networks are robust to weight binarization and other non-linear distortions
P. Merolla, R. Appuswamy, J. Arthur, S. K. Esser, and D. Modha · 2016
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Pruning convolutional neural networks for resource efficient transfer learning
P. Molchanov, S. Tyree, T. Karras, T. Aila, and J. Kautz · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
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Scalable compression of deep neural networks
X. Wang and J. Liang · 2016
Cited alongside, same era.
Quantization and training of low Bit-Width convolutional neural networks for object detection
P. Yin, S. Zhang, Y. Qi, and J. Xin · 2016
Cited alongside, same era.
S. Han, H. Mao, and W. J. Dally
Cited in the paper.
Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally
Cited in the paper.
Z. Cai, X. He, J. Sun, and N. Vasconcelos · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
P. Goyal, P. Dollár, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia, and K. He · 2017
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BitNet: Bit-regularized deep neural networks
A. Raghavan, M. Amer, S. Chai, and G. W. Taylor · 2017
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Fine-Pruning: Joint Fine-Tuning and compression of a convolutional network with bayesian optimization
F. Tung, S. Muralidharan, and G. Mori · 2017
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Attacking binarized neural networks
A. Galloway, G. W. Taylor, and M. Moussa · 2018
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