Abstract: Gaussian Process regression is a powerful non-parametric approach that facilitates probabilistic uncertainty quantification in machine learning. Distributed Gaussian Process (DGP) methods ...

Extended object tracking (EOT) is a prominent research area in high-resolution radar surveillance, ship tracking, and video tracking. However, EOT algorithms are susceptible to non-Gaussian noise from ...

Researchers in Japan have developed an adaptive motion reproduction system that allows robots to generate human-like movements using surprisingly small amounts of training data. Despite rapid advances ...

Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical ...

This important work introduces a family of interpretable Gaussian process models that allows us to learn and model sequence-function relationships in biomolecules. These models are applied to three ...

ClickFix attacks have evolved to feature videos that guide victims through the self-infection process, a timer to pressure targets into taking risky actions, and automatic detection of the operating ...

ABSTRACT: This paper introduces a method to develop a common model based on machine learning (ML) that predicts the mechanical behavior of a family with three composite materials. The latter are ...

Master the basics of visual composition with this full-step tutorial designed for beginners. Learn how to balance elements, create focal points, and guide the viewer’s eye using proven techniques like ...