IEEE

Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems

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 ...
IEEE

GDM-Net++: Multi-robot 2D and 3D Gas Distribution Mapping Via Deep Q-Learning and Gaussian Process Regression

Abstract: Gas distribution mapping (GDM) refers to the task of mapping the gas concentrations of an airborne chemical over a region of interest. A mobile robot equipped with a gas sensor can be used ...
GitHub

A minimal, portable Gaussian Splatting sandbox for rapid small-scale experimentation and performance insights in pure Python.

Gaussian Splatting is a cutting-edge 3D representation technique that models a scene as a set of learnable 3D Gaussian primitives. Each Gaussian defines a point in space with position, color, opacity, ...
GitHub

SVGPR: Stochastic Variational Gaussian Process Regression

A python package for scalable Gaussian process regression, allowing for simultaneous inference of both a dataset's latent function and input-dependent noise profile. Originally developed for ...
Scientific Research Publishing

Rasmussen, C.E. and Williams, C.K.I. (2005) Gaussian Processes for Machine Learning. The MIT Press.

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 ...