Diffusion sampler framework produces intrinsically calibrated predictive uncertainty for industrial soft sensors and process models via faithful posterior sampling.
What uncertainties do we need in Bayesian deep learning for computer vision?
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Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler
Diffusion sampler framework produces intrinsically calibrated predictive uncertainty for industrial soft sensors and process models via faithful posterior sampling.