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…
Rectified Linear Units (ReLUs) are among the most widely used activation function in a broad variety of tasks in vision. Recent theoretical results suggest that despite their excellent practical performance, in various cases, a substitution with basis expansions (e.g., polynomials) can yield signifi…