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Compression is a key step to deploy large neural networks on resource-constrained platforms.
Approximation by superposition of sigmoidal and radial basis functions
Hrushikesh N Mhaskar and Charles A Micchelli · 1992
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 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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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Sparse convolutional neural networks
Baoyuan Liu, Min Wang, Hassan Foroosh, Marshall Tappen, and Marianna Pensky · 2015
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Empirical evaluation of rectified activations in convolutional network
Bing Xu, Naiyan Wang, Tianqi Chen, and Mu Li · 2015
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Towards the limit of network quantization
Yoojin Choi, Mostafa El-Khamy, and Jungwon Lee · 2016
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Hardware-oriented approximation of convolutional neural networks
Philipp Gysel, Mohammad Motamedi, and Soheil Ghiasi · 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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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Ask me anything: Dynamic memory networks for natural language processing
Ankit Kumar, Ozan Irsoy, Peter Ondruska, Mohit Iyyer, James Bradbury, Ishaan Gulrajani, Victor Zhong, Romain Paulus, and Richard Socher · 2016
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Fengfu Li, Bo Zhang, and Bin Liu · 2016
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Why deep neural networks for function approximation?
Shiyu Liang and R Srikant · 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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Provable approximation properties for deep neural networks
Uri Shaham, Alexander Cloninger, and Ronald R Coifman · 2016
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Learning structured sparsity in deep neural networks
Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2016
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
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Efficient processing of deep neural networks: A tutorial and survey
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, and Joel Emer · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Error bounds for approximations with deep ReLU networks
Dmitry Yarotsky · 2017
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High-performance video content recognition with long-term recurrent convolutional network for fpga
Xiaofan Zhang, Xinheng Liu, Anand Ramachandran, Chuanhao Zhuge, Shibin Tang, Peng Ouyang, Zuofu Cheng, Kyle Rupnow, and Deming Chen · 2017
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Theoretical properties for neural networks with weight matrices of low displacement rank
Liang Zhao, Siyu Liao, Yanzhi Wang, Jian Tang, and Bo Yuan · 2017
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Scaling neural network performance through customized hardware architectures on reconfigurable logic
Michaela Blott, Thomas B Preußer, Nicholas Fraser, Giulio Gambardella, Kenneth O’Brien, Yaman Umuroglu, and Miriam Leeser · 2017
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Learning efficient object detection models with knowledge distillation
Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, and Manmohan Chandraker · 2017
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Universal function approximation by deep neural nets with bounded width and relu activations
Boris Hanin · 2017
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Extremely low bit neural network: Squeeze the last bit out with admm
Cong Leng, Hao Li, Shenghuo Zhu, and Rong Jin · 2017
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Training quantized nets: A deeper understanding
Hao Li, Soham De, Zheng Xu, Christoph Studer, Hanan Samet, and Tom Goldstein · 2017
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Learning discrete weights using the local reparameterization trick
Oran Shayar, Dan Levi, and Ethan Fetaya · 2017
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Neural network with unbounded activation functions is universal approximator
Sho Sonoda and Noboru Murata · 2017
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Towards effective low-bitwidth convolutional neural networks
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Recent advances in efficient computation of deep convolutional neural networks
Jian Cheng, Pei-song Wang, Gang Li, Qing-hao Hu, and Han-qing Lu · 2018
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Syq: Learning symmetric quantization for efficient deep neural networks
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Amc: Automl for model compression and acceleration on mobile devices
Yihui He, Ji Lin, Zhijian Liu, Hanrui Wang, Li-Jia Li, and Song Han · 2018
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Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Accuracy to throughput trade-offs for reduced precision neural networks on reconfigurable logic
Jiang Su, Nicholas J Fraser, Giulio Gambardella, Michaela Blott, Gianluca Durelli, David B Thomas, Philip HW Leong, and Peter YK Cheung · 2018
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Design flow of accelerating hybrid extremely low bit-width neural network in embedded fpga
Junsong Wang, Qiuwen Lou, Xiaofan Zhang, Chao Zhu, Yonghua Lin, and Deming Chen · 2018
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