A von Mises-Fisher posterior on weight directions, matched to normalized networks, yields a per-layer effective noise parameter with a closed-form dimension-aware KL that improves calibration on CIFAR-10.
Hyperspherical weight uncertainty in neural net- works
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Precise Bayesian Neural Networks
A von Mises-Fisher posterior on weight directions, matched to normalized networks, yields a per-layer effective noise parameter with a closed-form dimension-aware KL that improves calibration on CIFAR-10.