Tag: uncertainty quantification

A new Perspective argues that healthcare AI fails in clinics because trustworthiness is neglected in academic research design, and proposes ...

A new deep learning framework for river flow forecasting puts flood peaks, uncertainty, and interpretability at the center of model ...

Researchers at Hanoi University of Science and Technology have developed a fuzzy-set-based hesitation score that makes multi-task AI models robust ...

A self-supervised Vision Transformer framework that fuses multi-scale geological maps with aeromagnetic data has substantially outperformed conventional methods in mapping ...

A new hybrid 3D CNN-graph attention framework with entropy-guided fusion achieved an AUROC of 0.964 and delineated five new exploration ...

Researchers have released evidencelib, an open-source Python library that unifies Dempster–Shafer and Dezert–Smarandache evidence modeling, high-conflict fusion rules, and decision ...

Researchers have developed a unified scoring method that combines multiple accuracy metrics with model confidence to provide a more clinically ...

A new vertically augmented three-dimensional Empirical Bayesian Kriging framework reconstructs subsurface soil conditions with dramatically reduced error, cutting predictive error ...

Researchers have combined Bayesian physics-informed neural networks with Stein variational gradient descent to deliver fast, probabilistic forecasts of contaminant transport ...

A new digital twin framework uses ensemble forecasting and a fast, explainable confidence index to defer non-critical water maintenance during ...