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Recently there has been significant interest in training machine-learning models at low precision: by reducing precision, one can reduce computation and communication by one order of magnitude.
Svm versus least squares svm
Ye, Jieping and Xiong, Tao · 2007
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Measurement error in nonlinear models: A modern perspective (2nd ed.)
Hall, Daniel B · 2008
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On the impact of kernel approximation on learning accuracy
Cortes, Corinna, Mohri, Mehryar, and Talwalkar, Ameet · 2010
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Hardware acceleration of iterative image reconstruction for x-ray computed tomography
Kim, Jung Kuk, Zhang, Zhengya, and Fessler, Jeffrey A · 2011
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Improving the speed of neural networks on cpus
Vanhoucke, Vincent, Senior, Andrew, and Mao, Mark Z · 2011
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Chebyshev polynomial approximation for activation sigmoid function
Vlcek, Miroslav · 2012
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One-bit compressed sensing: Provable support and vector recovery
Gopi, Sivakant, Netrapalli, Praneeth, Jain, Prateek, and Nori, Aditya · 2013
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Learning with noisy labels
Natarajan, Nagarajan, Dhillon, Inderjit S, Ravikumar, Pradeep K, and Tewari, Ambuj · 2013
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Compressing deep convolutional networks using vector quantization
Gong, Yunchao, Liu, Liu, Yang, Ming, and Bourdev, Lubomir · 2014
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1-bit stochastic gradient descent and application to data-parallel distributed training of speech dnns
Seide, Frank, Fu, Hao, Droppo, Jasha, Li, Gang, and Yu, Dong · 2014
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Fast and near-optimal algorithms for approximating distributions by histograms
Acharya, Jayadev, Diakonikolas, Ilias, Hegde, Chinmay, Li, Jerry Zheng, and Schmidt, Ludwig · 2015
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Convex optimization: Algorithms and complexity
Bubeck, Sébastien · 2015
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Taming the wild: A unified analysis of hogwild-style algorithms
De Sa, Christopher M, Zhang, Ce, Olukotun, Kunle, and Ré, Christopher · 2015
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Deep learning with limited numerical precision
Gupta, Suyog, Agrawal, Ankur, Gopalakrishnan, Kailash, and Narayanan, Pritish · 2015
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Compression of deep convolutional neural networks for fast and low power mobile applications
Kim, Yong-Deok, Park, Eunhyeok, Yoo, Sungjoo, Choi, Taelim, Yang, Lu, and Shin, Dongjun · 2015
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Kim, Minje and Smaragdis, Paris · 2016
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Li, Fengfu, Zhang, Bo, and Liu, Bin · 2016
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Fixed point quantization of deep convolutional networks
Lin, Darryl, Talathi, Sachin, and Annapureddy, Sreekanth · 2016
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Convolutional neural networks using logarithmic data representation
Miyashita, Daisuke, Lee, Edward H, and Murmann, Boris · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, Mohammad, Ordonez, Vicente, Redmon, Joseph, and Farhadi, Ali · 2016
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Alistarh, Dan, Li, Jerry, Tomioka, Ryota, and Vojnovic, Milan · 2016
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Faster principal component regression via optimal polynomial approximation to sgn (x)
Allen-Zhu, Zeyuan and Li, Yuanzhi · 2016
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Principal component projection without principal component analysis
Frostig, Roy, Musco, Cameron, Musco, Christopher, and Sidford, Aaron · 2016
Cited alongside, same era.
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Han, Song, Mao, Huizi, and Dally, William J · 2016
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
Hubara, Itay, Courbariaux, Matthieu, Soudry, Daniel, El-Yaniv, Ran, and Bengio, Yoshua · 2016
Cited alongside, same era.
http://caffe.berkeleyvision.org/gathered/examples/cifar10.html
Caffe CIFAR-10 tutorial
Cited in the paper.
Quantized convolutional neural networks for mobile devices
Wu, Jiaxiang, Leng, Cong, Wang, Yuhang, Hu, Qinghao, and Cheng, Jian · 2016
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Zhang, Hantian, Li, Jerry, Kara, Kaan, Alistarh, Dan, Liu, Ji, and Zhang, Ce · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Zhou, Shuchang, Wu, Yuxin, Ni, Zekun, Zhou, Xinyu, Wen, He, and Zou, Yuheng · 2016
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Fpga accelerated dense linear machine learning: A precision-convergence trade-off
Kara, Kaan, Alistarh, Dan, Zhang, Ce, Mutlu, Onur, and Alonso, Gustavo · 2017
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