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Deep Convolutional Neural Networks~(CNNs) offer remarkable performance of classifications and regressions in many high-dimensional problems and have been widely utilized in real-word cognitive applications.
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A unified architecture for natural language processing: Deep neural networks with multitask learning
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Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition
Ossama Abdel-Hamid, Abdel-rahman Mohamed, Hui Jiang, and Gerald Penn · 2012
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Do deep nets really need to be deep?
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Compressing deep convolutional networks using vector quantization
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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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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Fast convolutional nets with fbfft: A gpu performance evaluation
Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, and Yann LeCun · 2014
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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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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Training cnns with low-rank filters for efficient image classification
Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, and Antonio Criminisi · 2015
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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 · 2015
Network trimming: A data-driven neuron pruning approach towards efficient deep architectures
Hengyuan Hu, Rui Peng, Yu-Wing Tai, and Chi-Keung Tang · 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
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
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 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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Cheng Tai, Tong Xiao, Yi Zhang, Xiaogang Wang, et al · 2015
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The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction
Ross S Williamson, Maneesh Sahani, and Jonathan W Pillow · 2015
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Efficient and accurate approximations of nonlinear convolutional networks
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Deep residual learning for image recognition
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Doubly convolutional neural networks
Shuangfei Zhai, Yu Cheng, Zhongfei Mark Zhang, and Weining Lu · 2016
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Estimating mixture entropy with pairwise distances
Artemy Kolchinsky and Brendan D Tracey · 2017
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An entropy-based pruning method for cnn compression
Jian-Hao Luo and Jianxin Wu · 2017
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Opening the black box of deep neural networks via information
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On the information bottleneck theory of deep learning
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