Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors

Published: Sep 9, 2019
Abstract
With rapid adoption of deep learning in critical applications, the question of when and how much to trust these models often arises, which drives the need to quantify the inherent uncertainties. While identifying all sources that account for the stochasticity of models is challenging, it is common to augment predictions with confidence intervals to convey the expected variations in a model's behavior. We require prediction intervals to be...
Paper Details
Title
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval Predictors
Published Date
Sep 9, 2019
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