Evaluating Bayesian deep learning for radio galaxy classification
<|reference_start|>Evaluating Bayesian deep learning for radio galaxy classification: 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.<|reference_end|>
@article{mohan2024evaluating,
    title={Evaluating Bayesian deep learning for radio galaxy classification},
    author={Devina Mohan and Anna M. M. Scaife},
    journal={arXiv preprint arXiv:2405.18351},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18351},
    primaryClass={cs.LG astro-ph.IM}
}