Scientific Research Publishing

Rasmussen, C.E. and Williams, C.K.I. (2006) Gaussian Processes for Machine Learning. The MIT Press.

ABSTRACT: Bipolar disorder (BD) affects approximately 45 million individuals worldwide and is characterized by recurrent episodes of mania, hypomania, and depression, with an average diagnostic delay ...
EurekAlert!

Advances in adsorption processes driven by machine learning

This paper systematically reviews the research progress and application of machine learning in adsorption processes. By virtue of outstanding nonlinear modeling and data mining capabilities, machine ...
GitHub

vSWIM - A Virtual Solar Wind Monitor for Mars

This repository contains predictions of the solar wind upstream of Mars from late 2014 onwards as calculated with a predictive model and MAVEN spacecraft data. This model output is useful for ...
Phys.org

Machine learning helps detect roars from lion collars without recording actual audio

Roaring over long distances is a key behavior of lions. They communicate within prides as well as with other animals using distinct sequences of moans and grunts. Scientists from the GAIA Initiative ...
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 ...
National Academies of Sciences%2c Engineering%2c and Medicine

Making Machine Learning Safer in High-Stakes Settings

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
Scientific Research Publishing

C. E. Rasmussen and C. K. I. Williams, “Gaussian Processes for Machine Learning,” The MIT Press, New York, 2006.

ABSTRACT: In this paper, we focus on a type of inverse problem in which the data are expressed as an unknown function of the sought and unknown model function (or its discretised representation as a ...
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 ...
IEEE

Gaussian Process-Based Nonlinear Moving Horizon Estimation

Abstract: In this paper, we propose a novel Gaussian process-based moving horizon estimation (MHE) framework for unknown nonlinear systems. On the one hand, we approximate the system dynamics by the ...
Science Daily

Machine learning simplifies industrial laser processes

Laser-based metal processing enables the automated and precise production of complex components, whether for the automotive industry or for medicine. However, conventional methods require time- and ...
IEEE

Spatial Modeling with Automated Machine Learning and Gaussian Process Regression Techniques for Imputing Wafer Acceptance Test Data

Abstract: The Wafer Acceptance Test (WAT) is a significant quality control measurement in the semiconductor industry. However, because the WAT process can be time-consuming and expensive, sampling ...