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Deep neural networks have achieved remarkable success in a wide range of practical problems.
Optimal brain damage
Yann LeCun, John S Denker, Sara A Solla, Richard E Howard, and Lawrence D Jackel · 1989
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Second order derivatives for network pruning: Optimal brain surgeon
Babak Hassibi and David G Stork · 1993
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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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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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky and Geoffrey Hinton · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Ian J Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio · 2013
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Min Lin, Qiang Chen, and Shuicheng Yan · 2013
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Improving deep neural networks with probabilistic maxout units
Jost Tobias Springenberg and Martin Riedmiller · 2013
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Gradient hard thresholding pursuit for sparsity-constrained optimization
Xiao-Tong Yuan, Ping Li, and Tong Zhang · 2013
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Stochastic pooling for regularization of deep convolutional neural networks
Matthew D. Zeiler and Rob Fergus · 2013
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Learning activation functions to improve deep neural networks
Forest Agostinelli, Matthew Hoffman, Peter J. Sadowski, and Pierre Baldi · 2014
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Memory bounded deep convolutional networks
Maxwell D Collins and Pushmeet Kohli · 2014
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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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Compressing deep convolutional networks using vector quantization
Yunchao Gong, Liu Liu, Ming Yang, and Lubomir Bourdev · 2014
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Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2014
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Structured pruning of deep convolutional neural networks
Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung · 2015
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Song Han, Huizi Mao, and William J. Dally · 2015
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Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Deep networks with internal selective attention through feedback connections
Marijn F. Stollenga, Jonathan Masci, Faustino J. Gomez, and Jürgen Schmidhuber · 2014
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2014
Cited alongside, same era.
Deep learning with s-shaped rectified linear activation units
Xiaojie Jin, Chunyan Xu, Jiashi Feng, Yunchao Wei, Junjun Xiong, and Shuicheng Yan · 2015
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Compression of deep convolutional neural networks for fast and low power mobile applications
Yong-Deok Kim, Eunhyeok Park, Sungjoo Yoo, Taelim Choi, Lu Yang, and Dongjun Shin · 2015
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Fast convnets using group-wise brain damage
Vadim Lebedev and Victor Lempitsky · 2015
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Convolutional neural networks with intra-layer recurrent connections for scene labeling
Ming Liang, Xiaolin Hu, and Bo Zhang · 2015
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Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
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