4 accepted papers
Dosing models often use differential equations to model biological dynamics. Neural differential equations in particular can learn to predict the derivative of a process, which permits predictions at irregular points of time. However, this temporal flexibility often comes with a high sensitivity to…
Time series are often complex and rich in information but sparsely labeled and therefore challenging to model. In this paper, we propose a self-supervised framework for learning robust and generalizable representations for time series. Our approach, called Temporal Neighborhood Coding (TNC), takes a…
Blood pressure (BP) is a vital sign of the human body and an important parameter for early detection of cardiovascular diseases. It is usually measured using cuff-based devices or monitored invasively in critically-ill patients. This paper presents two techniques that enable continuous and noninvasi…
In this paper, we propose a new unsupervised domain adaptation method based on the Riemannian geometry of Symmetric Positive-Definite (SPD) matrices. The proposed domain adaptation is based on parallel transport (PT) and moments alignment. We show that this method facilitates meaningful comparisons…