2026
Uncertainty Estimation via Hyperspherical Confidence Mapping
ICLR 2026poster
Quantifying uncertainty in neural network predictions is essential for deploying models in high-stakes domains such as autonomous driving, healthcare, and manufacturing. While conventional approaches often depend on costly sampling or parametric distributional assumptions, we propose Hyperspherical…