Abstract: Inducing-point-based sparse variational approximation scales Gaussian process models to large datasets but tends to overestimate observation noise and underestimate posterior variance.

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 ...

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 ...

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 ...

Step-by-step process tutorial on how to draw clothes in motion—learn to capture dynamic folds, fabric flow, and movement for lively, realistic characters. As shutdown becomes 2nd longest ever, Johnson ...

Step-by-step process tutorial on how to draw superheroes—from sketching dynamic poses to adding powerful details and bold costumes for iconic characters. Trump administration looking to sell nearly ...

Random fields and Gaussian processes constitute fundamental frameworks in modern probability theory and spatial statistics, providing robust tools for modelling complex dependencies over space and ...

Department of Chemical Engineering, Imperial College London, London SW7 2AZ, U.K. Sargent Centre for Process Systems Engineering, Imperial College London, London SW7 2AZ, U.K.