Fetching the paper…
Reading the bibliography…
Dropout is one of the key techniques to prevent the learning from overfitting.
Bayesian learning for neural networks
Neal, Radford M · 1996
Earlier work this paper cites.
Pattern recognition and machine learning , volume 1
Bishop, Christopher M · 2006
Earlier work this paper cites.
Algebraic geometry and statistical learning theory , volume 25
Watanabe, Sumio · 2009
Earlier work this paper cites.
Bayesian prediction of tissue-regulated splicing using RNA sequence and cellular context
Xiong, Hui Yuan, Barash, Yoseph, and Frey, Brendan J · 2011
Earlier work this paper cites.
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, Geoffrey E, Srivastava, Nitish, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan R · 2012
Cited alongside, same era.
Adaptive dropout for training deep neural networks
Ba, Jimmy and Frey, Brendan · 2013
Cited alongside, same era.
Understanding dropout
Baldi, Pierre and Sadowski, Peter J · 2013
Cited alongside, same era.
Dropout training as adaptive regularization
Wager, Stefan, Wang, Sida, and Liang, Percy · 2013
Cited alongside, same era.
Regularization of neural networks using dropconnect
Wan, Li, Zeiler, Matthew, Zhang, Sixin, Cun, Yann L, and Fergus, Rob · 2013
Later among the works it cites.
Fast dropout training
Wang, Sida and Manning, Christopher · 2013
Later among the works it cites.
Spatially-sparse convolutional neural networks
Graham, Benjamin · 2014
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…