arxiv
PublishedJune 18, 2026 at 4:00 AM
Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning
Publisher summary· verbatim
arXiv:2606.16214v2 Announce Type: replace-cross Abstract: Modern deep learning models remain notoriously prone to overconfidence, limiting their reliability in high-stakes applications. Bayesian methods aim to counter this by learning a distribution over model parameters, and recent advances now mak
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Originally published on arxiv ↗