arXiv Machine Learning August 14, 2026 By Yinsong Chen, Samson S. Yu, Kashem M. Muttaqi

Post-Hoc Uncertainty-Aware Explanations for Deployed Power Quality Disturbance Classifiers via Laplace Approximation

Read the original on arXiv Machine Learning →

arXiv:2604. 13658v2 Announce Type: replace Abstract: Deep learning classifiers achieve high accuracy in power quality disturbance (PQD) recognition, but existing explanation methods return a single deterministic attribution map and provide no measure of its reliability.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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