We experimentally show higher accuracy in gradient estimation and demonstrate a more stable and better performing training in deep convolutional modelswithbothproposedmethods.

Based on this insight, we examine ifcalibration can be improved by addressing thoseadversarially unrobustinputs.

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Smooth convex optimization problems over polytopes are an important class of problems that appear in many settings, such as low-rank matrix completion [1],structured supervised learning [2,3],electrical flowsovergraphs [4],video co-localization in computer vision [5], traffic assignment problems [6], and submodular function minimization [7].