Looking at the posterior: accuracy and uncertainty of neural-network predictions

Looking at the posterior: accuracy and uncertainty of neural-network predictions

Bayesian inference can quantify uncertainty in the predictions of neural networks using posterior distributions for model parameters and network output. By looking at these posterior distributions, one can separate the origin of uncertainty into aleatoric and epistemic contributions. One goal of unc...

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Bibliographic Details
Main Authors: Hampus Linander, Oleksandr Balabanov, Henry Yang, Bernhard Mehlig
Format: Article
Language:English
Published: IOP Publishing 2023-01-01
Series:Machine Learning: Science and Technology
Subjects:
deep learning
uncertainty quantification
bayesian inference
neural networks
active learning
Online Access:https://doi.org/10.1088/2632-2153/ad0ab4

Internet

https://doi.org/10.1088/2632-2153/ad0ab4

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