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Dropout (neural networks)

Dropout is a regularization technique patented by Google [1] for reducing overfitting in neural networks by preventing complex co-adaptations on training data. It is a very efficient way of performing model averaging with neural networks.[2] The term "dropout" refers to dropping out units (both hidden and visible) in a neural network.[3][4]

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ReferencesEdit

  1. ^ {{cite patent|title=System and method for addressing overfitting in a neural network |https://patents.google.com/patent/US9406017B2/en}
  2. ^ Hinton, Geoffrey E.; Srivastava, Nitish; Krizhevsky, Alex; Sutskever, Ilya; Salakhutdinov, Ruslan R. (2012). "Improving neural networks by preventing co-adaptation of feature detectors". arXiv:1207.0580 [cs.NE].
  3. ^ "Dropout: A Simple Way to Prevent Neural Networks from Overfitting". Jmlr.org. Retrieved July 26, 2015.
  4. ^ Warde-Farley, David; Goodfellow, Ian J.; Courville, Aaron; Bengio, Yoshua (2013-12-20). "An empirical analysis of dropout in piecewise linear networks". arXiv:1312.6197 [stat.ML].