Stochastic Spectral Descent for Discrete Graphical Models.

Interest in deep probabilistic graphical models has increased in recent years, due to their state-of-the-art performance on many machine learning applications. Such models are typically trained with the stochastic gradient method, which can take a significant number of iterations to converge. Since the computational cost of gradient estimation is prohibitive even for modestly sized models, trainin...