Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network using Truncated Gaussian Approximation

S. S. Shapiro and M. B. Wilk, “An analysis of variance test for normality (complete samples),”

1965

Earlier work this paper cites.

Prentice Hall New Jersey, 1994

J. G. Proakis, M. Salehi, N. Zhou, and X. Li, · 1994

Earlier work this paper cites.

Y. Bengio, N. Léonard, and A. Courville, “Estimating or propagating gradients through stochastic neurons for conditional computation,”

Original

2013

Earlier work this paper cites.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,”

2015

Earlier work this paper cites.

S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,” in

2015

Earlier work this paper cites.

S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,”

Original

2015

Earlier work this paper cites.

M. Courbariaux, Y. Bengio, and J.-P. David, “Binaryconnect: Training deep neural networks with binary weights during propagations,” in

2015

Earlier work this paper cites.

C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra, “Weight uncertainty in neural networks,”

Original

2015

Earlier work this paper cites.

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein,

Original

2015

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in

2016

Cited alongside, same era.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in

2016

Cited alongside, same era.

F. Li, B. Zhang, and B. Liu, “Ternary weight networks,”

Original

2016

Cited alongside, same era.

C. Zhu, S. Han, H. Mao, and W. J. Dally, “Trained ternary quantization,”

Original

2016

Cited alongside, same era.

M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1,”

Original

2016

Cited alongside, same era.

G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten, “Densely connected convolutional networks,” in

2017

Later among the works it cites.

J.-H. Luo, J. Wu, and W. Lin, “Thinet: A filter level pruning method for deep neural network compression,”

Original

2017

Later among the works it cites.

A. Mishra and D. Marr, “Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy,”

Original

2017

Later among the works it cites.

C. Leng, H. Li, S. Zhu, and R. Jin, “Extremely low bit neural network: Squeeze the last bit out with admm,”

Original

2017

Later among the works it cites.

X. Lin, C. Zhao, and W. Pan, “Towards accurate binary convolutional neural network,” in

2017

Later among the works it cites.