I don't understand the math completely but it looks like dropout can be derived from a Gaussian prior (approximating the Bernoulli) in a Bayesian context.
One useful tidbit is that you can get prediction intervals from deep learning models by running it forward N times with dropout and take the mean and variance of that distribution (plus another precision term).
One useful tidbit is that you can get prediction intervals from deep learning models by running it forward N times with dropout and take the mean and variance of that distribution (plus another precision term).