Evidential deep learning for uncertainty quantification and out-of-distribution detection in jet identification using deep neural networks

Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In this paper, we provide a detailed study of UQ based on evidential DL (EDL) for deep neural network models designed to ide...

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Autori principali:	Ayush Khot, Xiwei Wang, Avik Roy, Volodymyr Kindratenko, Mark S Neubauer
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	IOP Publishing 2025-01-01
Serie:	Machine Learning: Science and Technology
Soggetti:	jet classification machine learning deep learning evidential deep learning uncertainty quantification anomaly detection
Accesso online:	https://doi.org/10.1088/2632-2153/ade51b
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