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Universal approximation for differentiable maps now includes derivatives, unlocking new possibilities for function approximation in infinite-dimensional spaces.
Forget temperature scaling: JUCAL calibrates aleatoric and epistemic uncertainty in classifier ensembles, achieving SOTA results with significantly smaller ensembles and lower inference costs.
