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We investigate pruning and quantization for deep neural networks.
Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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Indoor segmentation and support inference from rgbd images
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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 Berg, and Li Fei-Fei · 2014
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Very deep convolutional networks for large-scale image recognition, 2014
Karen Simonyan and Andrew Zisserman · 2014
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Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights
Daniel Soudry, Itay Hubara, and Ron Meir · 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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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding, 2015
Song Han, Huizi Mao, and William J. Dally · 2015
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Learning both weights and connections for efficient neural networks
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Joint 3d estimation of vehicles and scene flow
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 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, 2016
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
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Ternary weight networks, 2016
Fengfu Li, Bo Zhang, and Bin Liu · 2016
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Pruning filters for efficient convnets
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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Pruning convolutional neural networks for resource efficient inference, 2016
Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila, and Jan Kautz · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Quantization and training of neural networks for efficient integer-arithmetic-only inference
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko · 2018
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Object scene flow
Moritz Menze, Christian Heipke, and Andreas Geiger · 2018
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Learning depth with convolutional spatial propagation network
Xinjing Cheng, Peng Wang, and Ruigang Yang · 2019
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Filter pruning via geometric median for deep convolutional neural networks acceleration
Yang He, Ping Liu, Ziwei Wang, Zhilan Hu, and Yi Yang · 2019
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Importance estimation for neural network pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, and Jan Kautz · 2019
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Towards efficient model compression via learned global ranking
Ting-Wu Chin, Ruizhou Ding, Cha Zhang, and Diana Marculescu · 2020
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Automatic differentiation in pytorch
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