3 accepted papers
Quantitative descriptions of confidence intervals and uncertainties of the predictions of a model are needed in many applications in vision and machine learning. Mechanisms that enable this for deep neural network (DNN) models are slowly becoming available, and occasionally, being integrated within…
Panel data involving longitudinal measurements of the same set of participants or entities taken over multiple time points is common in studies to understand early childhood development and disease modeling. Deep hybrid models that marry the predictive power of neural networks with physical simulato…
Uncertainty estimation in deep models is essential in many real-world applications and has benefited from developments over the last several years. Recent evidence suggests that existing solutions dependent on simple Gaussian formulations may not be sufficient. However, moving to other distributions…