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This paper aims to understand and improve the utility of the dropout operation from the perspective of game-theoretic interactions.
A value for n-person games
Lloyd S Shapley · 1953
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Weighted voting doesn’t work: A mathematical analysis
John F Banzhaf III · 1964
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Probabilistic values for games
Robert J Weber · 1988
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Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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An axiomatic approach to the concept of interaction among players in cooperative games
Michel Grabisch and Marc Roubens · 1999
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Detecting statistical interactions with additive groves of trees
Daria Sorokina, Rich Caruana, Mirek Riedewald, and Daniel Fink · 2008
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A. Krizhevsky and G. Hinton · 2009
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Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2012
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Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Dropout training as adaptive regularization
Stefan Wager, Sida I Wang, and Percy Liang · 2013
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Xiang Li, Shuo Chen, Xiaolin Hu, and Jian Yang · 2019
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