Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks

11 Jun 2019Qingyang WuHe LiLexin LiZhou Yu

With the widespread success of deep neural networks in science and technology, it is becoming increasingly important to quantify the uncertainty of the predictions produced by deep learning. In this paper, we introduce a new method that attaches an explicit uncertainty statement to the probabilities of classification using deep neural networks... (read more)

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