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 ...

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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 ...

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder, then ...