Specifically, we will perform comparisons to the other main types of discretisation methods considered in (Lindeberg, 2024b), based on either (i) explicit convolutions with sampled Gaussian derivative ...

Abstract: Gaussian processes (GPs) stand as crucial tools in machine learning and signal processing, with their effectiveness hinging on kernel design and hyperparameter optimization. This article ...

Gaussian mixture model (GMM), a class of unsupervised machine learning, is employed for environment perception based on a proposed vector approach to obstacle-free path planning. GMM circumscribes an ...

The Gaussian versions of all kernels had higher risks of large ... including kernel ridge regression, support vector machines (SVM), kernel fisher discriminant analysis (k-FDA), kernel logistic ...

This is the source code for the paper "Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots". Please see the companion blog ...

This repository contains libsinsp, libscap, the kernel module and the eBPF probes sources. These components are at the foundation of Falco and other projects that work with the same kind of data. To ...