[D] What makes variational dropout so popular for neural networks? • r/MachineLearning

Getting uncertainty is an important topic for neural networks. I, and many others, think that variational inference is the way forward. It seems that the most popular approach is to use a Bernoulli distribution for approximation. This follows mainly from the work of Yarin Gal, who shows that the Bernoulli approximation amounts to doing dropout. Another simple approximation is the Gaussian approximation.