Hyper-Diffusion: Estimating Epistemic and Aleatoric Uncertainty with a Single Model

Estimating and disentangling epistemic uncertainty (uncertainty that can be reduced with more training data) and aleatoric uncertainty (uncertainty that is inherent to the task at hand) is critically important when applying machine learning (ML) to high-stakes applications such as medical imaging and weather forecasting. Conditional diffusion models' breakthrough ability to accurately and efficiently sample from the posterior distribution of a dataset now makes uncertainty estimation conceptually straightforward: One need only train and sample from a large ensemble of diffusion models. …

applications cs.cv cs.lg data diffusion diffusion models forecasting imaging machine machine learning medical medical imaging training training data uncertainty weather weather forecasting