Dropout Training as Adaptive Regularization

Learning from hints in neural networks

Yaser S Abu-Mostafa · 1990

Earlier work this paper cites.

Noise injection into inputs in back-propagation learning

Kiyotoshi Matsuoka · 1992

Earlier work this paper cites.

Training with noise is equivalent to Tikhonov regularization

Chris M Bishop · 1995

Earlier work this paper cites.

Improving the accuracy and speed of support vector machines

Chris J.C. Burges and Bernhard Schölkopf · 1997

Earlier work this paper cites.

Theory of Point Estimation

Erich Leo Lehmann and George Casella · 1998

Earlier work this paper cites.

Transductive inference for text classification using support vector machines

Thorsten Joachims · 1999

Earlier work this paper cites.

Transformation invariance in pattern recognition: Tangent distance and propagation

Patrice Y Simard, Yann A Le Cun, John S Denker, and Bernard Victorri · 2000

Earlier work this paper cites.

Text classification from labeled and unlabeled documents using EM

Kamal Nigam, Andrew Kachites McCallum, Sebastian Thrun, and Tom Mitchell · 2000

Earlier work this paper cites.

The trade-off between generative and discriminative classifiers

G. Bouchard and B. Triggs · 2004

Earlier work this paper cites.

Classification with hybrid generative/discriminative models

R. Raina, Y. Shen, A. Ng, and A. McCallum · 2004

Cited alongside, same era.

Entropy regularization

Y. Grandvalet and Y. Bengio · 2005

Cited alongside, same era.

Semi-supervised structured output learning based on a hybrid generative and discriminative approach

J. Suzuki, A. Fujino, and H. Isozaki · 2007

Cited alongside, same era.

Adaptive regularization of weight vectors

Koby Crammer, Alex Kulesza, Mark Dredze, et al · 2009

Cited alongside, same era.

Adaptive subgradient methods for online learning and stochastic optimization

John Duchi, Elad Hazan, and Yoram Singer · 2010

Cited alongside, same era.

Regularization paths for generalized linear models via coordinate descent

Jerome Friedman, Trevor Hastie, and Rob Tibshirani · 2010

Cited alongside, same era.

Learning word vectors for sentiment analysis

Andrew L Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts · 2011

Later among the works it cites.

Large scale text classification using semi-supervised multinomial naive Bayes

Jiang Su, Jelber Sayyad Shirab, and Stan Matwin · 2011

Later among the works it cites.

Improving neural networks by preventing co-adaptation of feature detectors

Original

Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012

Later among the works it cites.

Baselines and bigrams: Simple, good sentiment and topic classification

Sida Wang and Christopher D Manning · 2012

Later among the works it cites.

Learning with marginalized corrupted features

Laurens van der Maaten, Minmin Chen, Stephen Tyree, and Kilian Q Weinberger · 2013

Closest in time.

Fast dropout training