This paper introduced Dropout, a new 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.

A much better paper, by the same authors but 2 years later, is [Dropout: a simple way to prevent neural networks from overfitting](http://www.shortscience.org/paper?bibtexKey=journals/jmlr/SrivastavaHKSS14).

Dropout can be interpreted as training an ensemble of many networks, which share weights.

It was notably used by [ImageNet Classification with Deep Convolutional Neural Networks](http://www.shortscience.org/paper?bibtexKey=krizhevsky2012imagenet).