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We analyze dropout in deep networks with rectified linear units and the quadratic loss.
Some infinity theory for predictor ensembles
L. Breiman · 2004
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Statistical behavior and consistency of classification methods based on convex risk minimization
T. Zhang · 2004
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Convexity, classification, and risk bounds
P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe · 2006
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Random classification noise defeats all convex potential boosters
P. M. Long and R. A. Servedio · 2010
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Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
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Deep learning how I did it: Merck 1st place interview, 2012
G. E. Dahl · 2012
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Dropout: a simple and effective way to improve neural networks, 2012
G. E. Hinton · 2012
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Improving neural networks by preventing co-adaptation of feature detectors, 2012
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
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Understanding dropout
Pierre Baldi and Peter J Sadowski · 2013
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Improving deep neural networks for LVCSR using rectified linear units and dropout
G. E. Dahl, T. N. Sainath, and G. E. Hinton · 2013
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Recent advances in deep learning for speech research at microsoft
L. Deng, J. Li, J. Huang, K. Yao, D. Yu, F. Seide, M. L. Seltzer, G. Zweig, X. He, J. Williams, Y. Gong, and A. Acero · 2013
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Maxout networks
Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron C. Courville, and Yoshua Bengio · 2013
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Dropout training as adaptive regularization
S. Wager, S. Wang, and P. Liang · 2013
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Regularization of neural networks using dropconnect
L. Wan, M. Zeiler, S. Zhang, Y. Le Cun, and R. Fergus · 2013
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Learning with pseudo-ensembles
P. Bachman, O. Alsharif, and D. Precup · 2014
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The dropout learning algorithm
P. Baldi and P. Sadowski · 2014
Cited alongside, same era.
Altitude training: Strong bounds for single-layer dropout
S. Wager, W. Fithian, S. Wang, and P. S. Liang · 2014
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Dropout as a Bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2015
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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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On the inductive bias of dropout
D. P. Helmbold and P. M. Long · 2015
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Norm-based capacity control in neural networks
Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro · 2015
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Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
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A fast and accurate dependency parser using neural networks
Danqi Chen and Christopher D Manning · 2014
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A convolutional neural network for modelling sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom · 2014
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Dropout: A simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Cited alongside, same era.
Follow the leader with dropout perturbations
T. Van Erven, W. Kotłowski, and M. K. Warmuth · 2014
Cited alongside, same era.
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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
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http://caffe.berkeleyvision.edu
Caffe, 2016 · 2016
Closest in time.
https://www.tensorflow.org
Tensorflow, 2016 · 2016
Closest in time.
http://torch.ch
Torch, 2016 · 2016
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