IEEE

Distributed Gaussian Processes With Uncertain Inputs

Abstract: Gaussian Process regression is a powerful non-parametric approach that facilitates probabilistic uncertainty quantification in machine learning. Distributed Gaussian Process (DGP) methods ...
SciELO

Extended Object Tracking Based on Gaussian Process in Non-Gaussian Noise Environment

Extended object tracking (EOT) is a prominent research area in high-resolution radar surveillance, ship tracking, and video tracking. However, EOT algorithms are susceptible to non-Gaussian noise from ...
IEEE

A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking

Abstract: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical ...
Hosted on MSN

Step-by-step process: How to draw faces from all angles

Want to draw faces from any angle? I’ll show you the step-by-step process to make it simple and fun! Perfect for all skill levels. #DrawingFaces #PortraitDrawingTips #ArtTutorial #SketchingFaces ...
eLife

Learning sequence-function relationships with scalable, interpretable Gaussian processes

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 ...
Bleeping Computer

ClickFix malware attacks evolve with multi-OS support, video tutorials

ClickFix attacks have evolved to feature videos that guide victims through the self-infection process, a timer to pressure targets into taking risky actions, and automatic detection of the operating ...
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 ...
C&EN

Efficient Implementation of Gaussian Process Regression Accelerated Saddle Point Searches with Application to Molecular Reactions

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
Phys.org

Gaussian processes provide a new path toward quantum machine learning

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 ...
C&EN

Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material Designs

Developing novel materials drives significant breakthroughs across various engineering fields. Recent advancements in computational resources and techniques have enabled comprehensive material ...