On the Implicit Bias of Dropout

@InProceedings{pmlr-v80-mianjy18b,
  title = 	 {On the Implicit Bias of Dropout},
  author =       {Mianjy, Poorya and Arora, Raman and Vidal, Rene},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {3540--3548},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v80/mianjy18b/mianjy18b.pdf},
  url = 	 {https://proceedings.mlr.press/v80/mianjy18b.html},
  abstract = 	 {Algorithmic approaches endow deep learning systems with implicit bias that helps them generalize even in over-parametrized settings. In this paper, we focus on understanding such a bias induced in learning through dropout, a popular technique to avoid overfitting in deep learning. For single hidden-layer linear neural networks, we show that dropout tends to make the norm of incoming/outgoing weight vectors of all the hidden nodes equal. In addition, we provide a complete characterization of the optimization landscape induced by dropout.}
}