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Dropout, a simple and effective way to train deep neural networks, has led to a number of impressive empirical successes and spawned many recent theoretical investigations.
On the stability of inverse problems
Andrey Nikolayevich Tikhonov · 1943
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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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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Maximum-margin matrix factorization
Nathan Srebro, Jason Rennie, and Tommi S Jaakkola · 2004
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Differential sparse coding
David M Bradley and J Andrew Bagnell · 2008
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky · 2009
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A practical guide to training restricted boltzmann machines
Geoffrey Hinton · 2010
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
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Understanding dropout
Pierre Baldi and Peter J Sadowski · 2013
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Improving neural networks with dropout
Nitish Srivastava · 2013
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Dropout training as adaptive regularization
Stefan Wager, Sida Wang, and Percy S Liang · 2013
Cited alongside, same era.
Sida Wang and Christopher Manning · 2013
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The dropout learning algorithm
Pierre Baldi and Peter Sadowski · 2014
Cited alongside, same era.
Dropout training for support vector machines
Ning Chen, Jun Zhu, Jianfei Chen, and Bo Zhang · 2014
Cited alongside, same era.
Dropout rademacher complexity of deep neural networks
Wei Gao and Zhi-Hua Zhou · 2014
Cited alongside, same era.
Dropout as a bayesian approximation: Insights and applications
Yarin Gal and Zoubin Ghahramani · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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To drop or not to drop: Robustness, consistency and differential privacy properties of dropout
Prateek Jain, Vivek Kulkarni, Abhradeep Thakurta, and Oliver Williams · 2015
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Variational dropout and the local reparameterization trick
Diederik P Kingma, Tim Salimans, and Max Welling · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep learning in neural networks: An overview
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David P Helmbold and Philip M Long · 2014
Cited alongside, same era.
Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 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
Cited alongside, same era.
Altitude training: Strong bounds for single-layer dropout
Stefan Wager, William Fithian, Sida Wang, and Percy S Liang · 2014
Cited alongside, same era.
On the accuracy of self-normalized log-linear models
Jacob Andreas, Maxim Rabinovich, Michael I Jordan, and Dan Klein · 2015
Cited alongside, same era.
Jürgen Schmidhuber · 2015
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Dropout distillation
Samuel Rota Bulò, Lorenzo Porzi, and Peter Kontschieder · 2016
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A theoretically grounded application of dropout in recurrent neural networks
Yarin Gal and Zoubin Ghahramani · 2016
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Fundamental differences between dropout and weight decay in deep networks
David P Helmbold and Philip M Long · 2016
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End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF
Xuezhe Ma and Eduard Hovy · 2016
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