Assessing the Robustness of Bayesian Dark Knowledge to Posterior Uncertainty

Further, the authors show that this approach is into a single network representing the posterior successful in the classification setting using a student network predictive distribution. Further, the authors whose architecture matches that of a single network show that this approach is successful in the classification in the teacher ensemble. The Bayesian Dark Knowledge setting using a student network whose architecture method also uses online learning of the student model based matches that of a single network in the on single samples from the parameter posterior, resulting in teacher ensemble. In this work, we examine the a training algorithm that requires only twice as much space robustness of Bayesian Dark Knowledge to higher as a standard point estimate-based learning procedure.