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Recent machine learning methods use increasingly large deep neural networks to achieve state of the art results in various tasks.
Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
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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, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Estimating or propagating gradients through stochastic neurons for conditional computation
Y. Bengio, N. Léonard, and A. Courville · 2013
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Exploiting linear structure within convolutional networks for efficient evaluation
E. L. Denton, W. Zaremba, J. Bruna, Y. LeCun, and R. Fergus · 2014
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Compressing deep convolutional networks using vector quantization
Y. Gong, L. Liu, M. Yang, and L. Bourdev · 2014
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Fixed-point feedforward deep neural network design using weights+ 1, 0, and- 1
K. Hwang and W. Sung · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights
D. Soudry, I. Hubara, and R. Meir · 2014
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Sequence to sequence learning with neural networks
I. Sutskever, O. Vinyals, and Q. V. Le · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
M. Courbariaux, Y. Bengio, and J.-P. David · 2015
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High-performance hardware for machine learning
W. Dally · 2015
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S. Han, H. Mao, and W. J. Dally · 2015
Cited alongside, same era.
Learning both weights and connections for efficient neural network
S. Han, J. Pool, J. Tran, and W. Dally · 2015
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
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M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko · 2017
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In-datacenter performance analysis of a tensor processing unit
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, et al · 2017
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Performance guaranteed network acceleration via high-order residual quantization
Z. Li, B. Ni, W. Zhang, X. Yang, and W. Gao · 2017
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer · 2016
Cited alongside, same era.
F. Li and B. Liu · 2016
Cited alongside, same era.
Fixed point quantization of deep convolutional networks
D. Lin, S. Talathi, and S. Annapureddy · 2016
Cited alongside, same era.
Xnor-net: Imagenet classification using binary convolutional neural networks
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Qsgd: Communication-efficient sgd via gradient quantization and encoding
D. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnovic · 2017
Cited alongside, same era.
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Towards accurate binary convolutional neural network
X. Lin, C. Zhao, and W. Pan · 2017
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Ternary neural networks with fine-grained quantization
N. Mellempudi, A. Kundu, D. Mudigere, D. Das, B. Kaul, and P. Dubey · 2017
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8-bits inference with tensorrt
S. Migacz · 2017
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Learning discrete weights using the local reparameterization trick
O. Shayar, D. Levi, and E. Fetaya · 2017
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Aciq: Analytical clipping for integer quantization of neural networks
R. Banner, Y. Nahshan, E. Hoffer, and D. Soudry · 2018
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Syq: Learning symmetric quantization for efficient deep neural networks
J. Faraone, N. Fraser, M. Blott, and P. H. Leong · 2018
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Model compression via distillation and quantization
A. Polino, R. Pascanu, and D. Alistarh · 2018
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