GP: A Gaussian Process regressor that models a function using a kernel. It updates with new data and makes predictions with associated uncertainty (mean and variance). ###3 Data Generator Class ...

Kernel-methods-and-gaussian-processes-applications This repository contains three Jupyter notebooks that demonstrate the use of Support Vector Machines (SVMs) with different kernel functions.

This paper revisits single-channel audio source separation based on a probabilistic generative model of a mixture signal defined in the continuous time domain. We assume that each source signal ...

Gaussian process regression is a sophisticated technique that uses what is called the kernel trick to deal with complex non-linear data, and L2 regularization to avoid model overfitting where a model ...

To address these issues, we propose MetaGP, a meta-learning-based Gaussian process latent variable model that uses a Gaussian process kernel function to capture long-term dependencies and to maintain ...

We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are learned from data ...