Bayesian deep learning has always garnered substantial interest in the machine learning community.

Evidential Deep Learning (EDL), grounded in Evidence Theory and Subjective Logic (SL), provides a robust framework to estimate uncertainty for out-of-distribution (OOD) detection alongside traditional classification probabilities.

Dosing models often use differential equations to model biological dynamics.

Uncertainty Quantification (UQ) is crucial for reliable image segmentation.

While extensive research on uncertainty estimation has been conducted with unimodal data, uncertainty estimation for multimodal data remains a challenge. Neural processes (NPs) have been demonstrated to be an effective uncertainty estimation method for unimodal data by providing the reliability of Gaussian processes with efficient and powerful DNNs.

Uncertainty estimation aims to evaluate the confidence of a trained deep neural network.