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Theoretical and empirical evidence indicates that the depth of neural networks is crucial for their success.
Computational limitations of small-depth circuits
Johan Håstad · 1987
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On the power of small-depth threshold circuits
Johan Håstad and Mikael Goldmann · 1991
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Untersuchungen zu dynamischen neuronalen Netzen
Sepp Hochreiter · 1991
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Learning complex, extended sequences using the principle of history compression
Jürgen Schmidhuber · 1992
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Bridging long time lags by weight guessing and “long short-term memory”
Sepp Hochreiter and Jurgen Schmidhuber · 1996
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Learning to forget: Continual prediction with LSTM
Felix A. Gers, Jürgen Schmidhuber, and Fred Cummins · 1999
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A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh · 2006
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Flexible, high performance convolutional neural networks for image classification
DC Ciresan, Ueli Meier, Jonathan Masci, Luca M Gambardella, and Jürgen Schmidhuber · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Multi-column deep neural networks for image classification
Dan Ciresan, Ueli Meier, and Jürgen Schmidhuber · 2012
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Training deep and recurrent networks with hessian-free optimization
James Martens and Ilya Sutskever · 2012
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Deep learning made easier by linear transformations in perceptrons
Tapani Raiko, Harri Valpola, and Yann LeCun · 2012
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Feature learning in deep neural networks-studies on speech recognition tasks
Dong Yu, Michael L. Seltzer, Jinyu Li, Jui-Ting Huang, and Frank Seide · 2013
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On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
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Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M. Saxe, James L. McClelland, and Surya Ganguli · 2013
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Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and Yoshua Bengio · 2013
Random walk initialization for training very deep feedforward networks
David Sussillo and L. F. Abbott · 2014
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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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Caffe: Convolutional architecture for fast feature embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell · 2014
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Spatially-sparse convolutional neural networks
Benjamin Graham · 2014
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Compete to compute
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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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
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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On the complexity of neural network classifiers: A comparison between shallow and deep architectures
Monica Bianchini and Franco Scarselli · 2014
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On the number of linear regions of deep neural networks
Guido F Montufar, Razvan Pascanu, Kyunghyun Cho, and Yoshua Bengio · 2014
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On the expressive efficiency of sum product networks
James Martens and Venkatesh Medabalimi · 2014
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Min Lin, Qiang Chen, and Shuicheng Yan · 2014
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Deep networks with internal selective attention through feedback connections
Marijn F Stollenga, Jonathan Masci, Faustino Gomez, and Jürgen Schmidhuber · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deeply-supervised nets
Chen-Yu Lee, Saining Xie, Patrick Gallagher, Zhengyou Zhang, and Zhuowen Tu · 2015
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Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber · 2015
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Nal Kalchbrenner, Ivo Danihelka, and Alex Graves · 2015
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Understanding locally competitive networks
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