Dropout: a simple way to prevent neural networks from overfitting Dropout: a simple way to prevent neural networks from overfitting
Paper summary This paper is a much better introduction to Dropout than [Improving neural networks by preventing co-adaptation of feature detectors](http://www.shortscience.org/paper?bibtexKey=journals/corr/1207.0580), written by the same authors two years later. ## General idea of Dropout Dropout is a layer type. It has a parameter $\alpha \in (0, 1)$. The output dimensionality of a dropout layer is equal to its input dimensionality. With a probability of $\alpha$ any neurons output is set to 0. At testing time, the output of all neurons is multiplied with $\alpha$ to compensate for the fact that no output is set to 0. ## Interpretations Dropout can be interpreted as training an ensemble of many networks, which share weights. It can also be seen as a regularizer.

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