A starting point.
Dropout regularizes neural networks by randomly omitting units during training, reducing dependence on particular combinations of features.
What to keep in mind
Read the original paper for the experimental setting, baselines, and limitations; a result in one setting is not a guarantee of performance elsewhere.
This work is included in a researcher’s reading path. A detailed editorial explanation is still being prepared. The complete manuscript is available in Full paper.
Source: Improving neural networks by preventing co-adaptation of feature detectors. The original manuscript contains the methods, experiments, figures, and references. An arXiv posting date may follow an earlier conference publication. Read the linked record for version history.