Decision-theoretic uncertainty quantification formalizes evaluation of generative domain adaptation trustworthiness for PPG-based atrial fibrillation classification by linking uncertainty to downstream task utility.
On calibrating diffusion probabilistic models,
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Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification
Decision-theoretic uncertainty quantification formalizes evaluation of generative domain adaptation trustworthiness for PPG-based atrial fibrillation classification by linking uncertainty to downstream task utility.