New estimators quantify deep learning uncertainty sources

2026-08-12

Researchers have adapted classical statistical estimators to quantify aleatoric and epistemic uncertainty in deep learning predictions. The approach leverages approximate Fisher Information Matrices for scalability.

Source: arXiv · cs.LG

Reported by VERA Newswire.