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Recent years have seen deep neural networks (DNNs) becoming wider and deeper to achieve better performance in many applications of AI.
Computers and Intractability : A Guide to the Theory of NP-Completeness
M. R. Garey and D. S. Johnson · 1979
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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A strip-packing algorithm with absolute performance bound 2
A. Steinberg · 1997
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A new placement heuristic for the orthogonal stock-cutting problem
Edmund K. Burke, Graham Kendall, and Glenn Whitwell · 2004
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An exact strip packing algorithm based on canonical forms
Yohei Arahori, Takashi Imamichi, and Hiroshi Nagamochi · 2012
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Theano: new features and speed improvements
Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian J. Goodfellow, Arnaud Bergeron, Nicolas Bouchard, David Warde-Farley, and Yoshua Bengio · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Compressing deep convolutional networks using vector quantization
Yunchao Gong, Liu Liu, Ming Yang, and Lubomir D. Bourdev · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
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MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William J. Dally · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey E. Hinton · 2015
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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 C. Berg, and Li Fei-Fei · 2015
Cited alongside, same era.
Going deeper with convolutions
Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
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Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding
Song Han, Huizi Mao, and William J. Dally · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design
Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, and Stephen W. Keckler · 2016
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Memory reduction method for deep neural network training
Koichi Shirahata, Yasumoto Tomita, and Atsushi Ike · 2016
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Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott E. Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
Cited alongside, same era.
Chainer: A next-generation open source framework for deep learning
Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton · 2015
Cited alongside, same era.
TensorFlow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
Cited alongside, same era.
Training deeper models by GPU memory optimization on TensorFlow
Chen Meng, Minmin Sun, Jun Yang, Minghui Qiu, and Ynag Gu · 2017
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Inception-v4, Inception-ResNet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A. Alemi · 2017
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SuperNeurons: Dynamic GPU memory management for training deep neural networks
Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, and Tim Kraska · 2018
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