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Found 2 papers, 1 papers with code
Hyper-Diffusion: Estimating Epistemic and Aleatoric Uncertainty with a Single Model
In this work we introduce a new approach to ensembling, hyper-diffusion, which allows one to accurately estimate epistemic and aleatoric uncertainty with a single model.
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
In order to encourage broader adoption of evidential deep learning in Earth System Science, we have developed a new Python package, MILES-GUESS (https://github. com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.
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