Project for 10-707: Deep Learning @ Carnegie Mellon University.
Proposed and implemented a new dropout method that generates the mask by sampling from a multivariate Gaussian distribution, whose parameters are dynamically estimated from statistics calculated over a mini-batch of training data.
Conducted experiments on MNIST & CIFAR-10 datasets and showed that the proposed method can improve performance in terms of both convergence speed and generalization capability.