Objective: This paper presents a Transfer Learning approach for dealing with the statistical
variability of electroencephalographic (EEG) signals recorded on different sessions and/or …

Pretraining molecular representation models without labels is fundamental to various
applications. Conventional methods mainly process 2D molecular graphs and focus solely …

Covariance matrices have attracted attention for machine learning applications due to their
capacity to capture interesting structure in the data. The main challenge is that one needs to …

Principal component analysis (PCA) is a commonly used pattern analysis method that maps
high-dimensional data into a lower-dimensional space maximizing the data variance, that …

Abstract Today, state-of-the-art deep neural networks that process events first convert them
into dense, grid-like input representations before using an off-the-shelf network. However …

Optimal transport aligns samples across distributions by minimizing the transportation cost
between them, eg, the geometric distances. Yet, it ignores coherence structure in the data …

In this paper, we propose a novel method for representation and classification of two-person
interactions from 3D skeleton sequences. The key idea of our approach is to use Gaussian …

This article presents an overview of robot learning and adaptive control applications that can
benefit from a joint use of Riemannian geometry and probabilistic representations. The roles …

Used batteries from electric vehicles (EVs) can be utilized as retired battery energy storage
systems (RBESSs) at battery swapping and charging stations (BSCSs) to enhance their …

We develop an advanced approach for extending Gaussian Differential Privacy (GDP) to
general Riemannian manifolds. The concept of GDP stands out as a prominent privacy …