GitHub

Gaussian Process State-Space Models

This repository provides a minimal, research-grade implementation of sparse variational Gaussian process state-space models (GPSSMs). The goal is convenience: generate time series, train a GPSSM, ...
GitHub

SVGP-KAN: Sparse Variational Gaussian Process Kolmogorov-Arnold Networks

SVGP-KAN is a library for building interpretable, probabilistic, and scalable neural networks. It merges the architecture of Kolmogorov-Arnold Networks (KANs) with the uncertainty quantification of ...
IEEE

Sequential training of semi-supervised classification based on sparse Gaussian process regression

Abstract: Gaussian process regression (GPR) is a very important Bayesian approach in machine learning applications. It has been extensively used in semi-supervised learning tasks. In this paper, we ...
DLR

Video: Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes

This video presents an uncertainty estimation algorithm for planetary exploration aerial robots - ARDEA. The robot detects rovers and landers semantically. The key technology behind is a sparse ...
IEEE

Distributed Online Sparse Gaussian Process Regression for Multi-Agent Coverage Control

Abstract: Multi-agent systems have been employed to perform optimal coverage control in unknown environment by predicting the unknown density function in an online fashion. However, traditional ...