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Quantization reduces computation costs of neural networks but suffers from performance degeneration.
Statistical mechanics of learning from examples
Hyunjune Sebastian Seung, Haim Sompolinsky, and Naftali Tishby · 1992
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Stochastic processes in physics and chemistry
Nicolaas Godfried Van Kampen · 1992
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The statistical mechanics of learning a rule
Timothy LH Watkin, Albrecht Rau, and Michael Biehl · 1993
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Song Han, Huizi Mao, and William J Dally · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
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Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Fengfu Li, Bo Zhang, and Bin Liu · 2016
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
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Exponential expressivity in deep neural networks through transient chaos
Ben Poole, Subhaneil Lahiri, Maithra Raghu, Jascha Sohl-Dickstein, and Surya Ganguli · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
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Samuel S Schoenholz, Justin Gilmer, Surya Ganguli, and Jascha Sohl-Dickstein · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
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Deep learning with low precision by half-wave gaussian quantization
Zhaowei Cai, Xiaodong He, Jian Sun, and Nuno Vasconcelos · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Computer vision for autonomous vehicles: Problems, datasets and state-of-the-art
Joel Janai, Fatma Güney, Aseem Behl, and Andreas Geiger · 2017
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Learning efficient convolutional networks through network slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, and Changshui Zhang · 2017
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Deep learning for computer vision: A brief review
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis · 2018
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Mixed precision quantization of convnets via differentiable neural architecture search
Bichen Wu, Yanghan Wang, Peizhao Zhang, Yuandong Tian, Peter Vajda, and Kurt Keutzer · 2018
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Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S Schoenholz, and Jeffrey Pennington · 2018
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Alternating multi-bit quantization for recurrent neural networks
Chen Xu, Jianqiang Yao, Zhouchen Lin, Wenwu Ou, Yuanbin Cao, Zhirong Wang, and Hongbin Zha · 2018
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Recent trends in deep learning based natural language processing [review article]
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Charles H Martin and Michael W Mahoney · 2017
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Ternary neural networks with fine-grained quantization
Naveen Mellempudi, Abhisek Kundu, Dheevatsa Mudigere, Dipankar Das, Bharat Kaul, and Pradeep Dubey · 2017
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Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy
Asit Mishra and Debbie Marr · 2017
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Wrpn: wide reduced-precision networks
Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, and Debbie Marr · 2017
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Weighted-entropy-based quantization for deep neural networks
Eunhyeok Park, Junwhan Ahn, and Sungjoo Yoo · 2017
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Mean field residual networks: On the edge of chaos
Ge Yang and Samuel Schoenholz · 2017
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Balanced quantization: An effective and efficient approach to quantized neural networks
Shu-Chang Zhou, Yu-Zhi Wang, He Wen, Qin-Yao He, and Yu-Heng Zou · 2017
Cited alongside, same era.
Proxquant: Quantized neural networks via proximal operators
Yu Bai, Yu-Xiang Wang, and Edo Liberty · 2018
Cited alongside, same era.
Tom Young, Devamanyu Hazarika, Soujanya Poria, and Erik Cambria · 2018
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Jiahui Yu, Linjie Yang, Ning Xu, Jianchao Yang, and Thomas Huang · 2018
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Lq-nets: Learned quantization for highly accurate and compact deep neural networks
Dongqing Zhang, Jiaolong Yang, Dongqiangzi Ye, and Gang Hua · 2018
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Learned step size quantization
Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S. Modha · 2019
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Differentiable soft quantization: Bridging full-precision and low-bit neural networks
Ruihao Gong, Xianglong Liu, Shenghu Jiang, Tianxiang Li, Peng Hu, Jiazhen Lin, Fengwei Yu, and Junjie Yan · 2019
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Sambhav R. Jain, Albert Gural, Michael Wu, and Chris Dick · 2019
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Autoqb: Automl for network quantization and binarization on mobile devices
Qian Lou, Lantao Liu, Minje Kim, and Lei Jiang · 2019
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Speech recognition using deep neural networks: A systematic review
Ali Bou Nassif, Ismail Shahin, Imtinan Attili, Mohammad Azzeh, and Khaled Shaalan · 2019
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Differentiable quantization of deep neural networks
Stefan Uhlich, Lukas Mauch, Kazuki Yoshiyama, Fabien Cardinaux, Javier Alonso Garcia, Stephen Tiedemann, Thomas Kemp, and Akira Nakamura · 2019
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Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
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A mean field theory of batch normalization
Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, and Samuel S Schoenholz · 2019
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Fixup initialization: Residual learning without normalization
Hongyi Zhang, Yann N Dauphin, and Tengyu Ma · 2019
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Binary ensemble neural network: More bits per network or more networks per bit?
Shilin Zhu, Xin Dong, and Hao Su · 2019
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Effective training of convolutional neural networks with low-bitwidth weights and activations
Bohan Zhuang, Jing Liu, Mingkui Tan, Lingqiao Liu, Ian Reid, and Chunhua Shen · 2019
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