  The application of stochastic variance reduction to optimization has shown
remarkable recent theoretical and practical success. The applicability of these
techniques to the hard non-convex optimization problems encountered during
training of modern deep neural networks is an open problem. We show that naive
application of the SVRG technique and related approaches fail, and explore why.
