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Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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.
Caffe: Convolutional architecture for fast feature embedding
Original
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi and et. al o · 2015
Earlier work this paper cites.
Deep speech 2: End-to-end speech recognition in english and mandarin
Original
Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, et al · 2015
Earlier work this paper cites.

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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients

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2016

Trained Ternary Quantization

Zhu, Chenzhuo, Han, Song, Mao, Huizi et al.

Understand

Deep neural networks are widely used in machine learning applications.

However, the deployment of large neural networks models can be difficult to deploy on mobile devices with limited power budgets.
To solve this problem, we propose Trained Ternary Quantization (TTQ), a method that can reduce the precision of weights in neural networks to ternary values.
This method has very little accuracy degradation and can even improve the accuracy of some models (32, 44, 56-layer ResNet) on CIFAR-10 and AlexNet on ImageNet.

Built on

Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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.
Caffe: Convolutional architecture for fast feature embedding
Original
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi and et. al o · 2015
Earlier work this paper cites.
Deep speech 2: End-to-end speech recognition in english and mandarin
Original
Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, et al · 2015
Earlier work this paper cites.

Similar

Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
Cited alongside, same era.
Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Original
Song Han, Huizi Mao, and William J Dally · 2015
Cited alongside, same era.
Deep residual learning for image recognition
Original
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Original
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Neural networks with few multiplications
Original

Then

Identity mappings in deep residual networks
Original
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Closest in time.
Quantized neural networks: Training neural networks with low precision weights and activations
Original
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
Closest in time.
Ternary weight networks
Original
Fengfu Li and Bin Liu · 2016
Closest in time.
Xnor-net: Imagenet classification using binary convolutional neural networks
Original
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Closest in time.
Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio · 2015

Cited alongside, same era.

ImageNet Large Scale Visual Recognition Challenge

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015

Cited alongside, same era.

Binarized neural networks: Training neural networks with weights and activations constrained to+ 1 or-

Matthieu Courbariaux, Itay Hubara, COM Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio

Cited in the paper.

Original

Shuchang Zhou, Zekun Ni, Xinyu Zhou, He Wen, Yuxin Wu, and Yuheng Zou · 2016

Closest in time.