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• W3163155043 abstract "Uncertainty awareness is crucial to develop reliable machine learning models. In this work, we propose the Natural Posterior Network (NatPN) for fast and high-quality uncertainty estimation for any task where the target distribution belongs to the exponential family. Thus, NatPN finds application for both classification and general regression settings. Unlike many previous approaches, NatPN does not require out-of-distribution (OOD) data at training time. Instead, it leverages Normalizing Flows to fit a single density on a learned low-dimensional and task-dependent latent space. For any input sample, NatPN uses the predicted likelihood to perform a Bayesian update over the target distribution. Theoretically, NatPN assigns high uncertainty far away from training data. Empirically, our extensive experiments on calibration and OOD detection show that NatPN delivers highly competitive performance for classification, regression and count prediction tasks." @default.
• W3163155043 created "2021-05-24" @default.
• W3163155043 creator A5000184599 @default.
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• W3163155043 creator A5074504351 @default.
• W3163155043 date "2021-05-10" @default.
• W3163155043 modified "2023-09-27" @default.
• W3163155043 title "Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions." @default.
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