When training deep neural networks, we must confront the challenges ofgeneral nonconvexoptimization problems.

The notion ofmargin,minimum distance toadecision boundary,has served as the foundation of several theoretically profound and empirically successful results for both classification and regression tasks.

Ensemble approaches for uncertainty estimation have recently been applied to the tasks of misclassification detection, out-of-distribution input detection and adversarial attack detection. Prior Networks have been proposed as an approach to efficiently emulate an ensemble of models for classification by parameteris-ing a Dirichlet prior distribution over output distributions.

The OOD results quoted in the papers use a range of classification network architectures.

Graph), a comprehensive benchmark of graph-based problem solving designed in natural language.

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