Evaluating Bayesian deep learning for radio galaxy classification

Published: 26 Apr 2024, Last Modified: 15 Jul 2024UAI 2024 posterEveryoneRevisionsBibTeXCC BY 4.0
Keywords: bayesian neural networks, variational inference, radio astronomy, distribution shift, uncertainty calibration
TL;DR: We present a domain-specific evaluation of Bayesian deep neural networks for radio astronomy
Abstract: The radio astronomy community is rapidly adopting deep learning techniques to deal with the huge data volumes expected from the next generation of radio observatories. Bayesian neural networks (BNNs) provide a principled way to model uncertainty in the predictions made by such deep learning models and will play an important role in extracting well-calibrated uncertainty estimates on their outputs. In this work, we evaluate the performance of different BNNs against the following criteria: predictive performance, uncertainty calibration and distribution-shift detection for the radio galaxy classification problem.
List Of Authors: Mohan, Devina and Scaife, Anna M M
Latex Source Code: zip
Signed License Agreement: pdf
Code Url: https://github.com/devinamhn/RadioGalaxies-BNNs
Submission Number: 750
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