Abstract:Traditional neural networks are simple to train but they produce overconfident predictions, while Bayesian neural networks provide good uncertainty quantification but optimizing them is time consuming. This paper introduces a new approach, direct uncertainty quantification (Direc-tUQ), that combines their advantages where the neural network directly models uncertainty in output space, and captures both aleatoric and epistemic uncertainty. DirectUQ can be derived as an alternative variational lower bound, and h… Show more
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