Corpus ID: 24865214

Differentially Private Variational Dropout

@article{Ermis2017DifferentiallyPV,
  title={Differentially Private Variational Dropout},
  author={Beyza Ermis and Ali Taylan Cemgil},
  journal={ArXiv},
  year={2017},
  volume={abs/1712.02629}
}
Deep neural networks with their large number of parameters are highly flexible learning systems. The high flexibility in such networks brings with some serious problems such as overfitting, and regularization is used to address this problem. A currently popular and effective regularization technique for controlling the overfitting is dropout. Often, large data collections required for neural networks contain sensitive information such as the medical histories of patients, and the privacy of the… Expand
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