AI RESEARCH
Uncertainty Estimation via Hyperspherical Confidence Mapping
arXiv CS.LG
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ArXi:2605.05964v1 Announce Type: new Quantifying uncertainty in neural network predictions is essential for high-stakes domains such as autonomous driving, healthcare, and manufacturing. While existing approaches often depend on costly sampling or restrictive distributional assumptions, we propose Hyperspherical Confidence Mapping (HCM), a simple yet principled framework for sampling-free and distribution-free uncertainty estimation.