Dropout: a simple way to prevent neural networks from overfitting

@article{Srivastava2014DropoutAS,
  title={Dropout: a simple way to prevent neural networks from overfitting},
  author={Nitish Srivastava and Geoffrey E. Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov},
  journal={J. Mach. Learn. Res.},
  year={2014},
  volume={15},
  pages={1929-1958}
}
Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem. The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units… CONTINUE READING

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