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

Abstract: Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs ...

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 ...

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 ...

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 ...

ABSTRACT: In this paper, we focus on a type of inverse problem in which the data are expressed as an unknown function of the sought and unknown model function (or its discretised representation as a ...

Step-by-step tutorial on how to draw faces from all angles, with easy techniques to master proportions, perspective, and expression for lifelike portraits. Emmanuel Macron Reacts To Trump's Hot Mic ...

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 ...

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 ...