Dropout (neural networks)
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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]
See also
References
- ↑ "System and method for addressing overfitting in a neural network" patent
- ↑ 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].
- ↑ "Dropout: A Simple Way to Prevent Neural Networks from Overfitting". http://jmlr.org/papers/v15/srivastava14a.html. Retrieved July 26, 2015.
- ↑ 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].
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