Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms

We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system. Three of the most common uncertainty quantification methods-Bayesian neural networks (BNNs), concrete dropout (CD), and deep ensembles (DEs) - are compared t...

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Bibliographic Details

Published in:	Machine learning: science and technology Vol. 2; no. 1; pp. 15002 - 15010
Main Authors:	Caldeira, João, Nord, Brian
Format:	Journal Article
Language:	English
Published:	Bristol IOP Publishing 01.03.2021
Subjects:	Algorithms Bayesian inference Deep learning ensemble of neural networks Error analysis Machine learning Neural networks Noise prediction Physical sciences Statistical methods Training Uncertainty uncertainty quantification
ISSN:	2632-2153, 2632-2153
Online Access:	Get full text
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