Results 71 to 80 of about 851,390 (284)
Dynamic switching control of buck converters using unsupervised machine learning methods
The Journal of Engineering, 2020 This study presents the implementation of a new unsupervised machine learning based system called a buck converter controller using unsupervised machine learning (ABCML) to control the operation of a type of switching voltage regulators, commonly called ...
Brook W. Abegaz
doaj +1 more source
Parameter Tuning Using Gaussian Processes [PDF]
, 2012 Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter settings generally result in poor ...
Ma, Jinjin
core +1 more source
Memory and Resting‐State Connectivity in Acute Transient Global Amnesia: A Case–Control fMRI Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background and Objectives Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network‐level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their ...
Elias El Otmani +10 more
wiley +1 more source
The Use of Genetic Algorithms for Searching Parameter Space in Gaussian Process Modeling
Journal of Telecommunications and Information Technology, 2015 The aim of the paper is to present the possibilities of modeling the experimental data by Gaussian processes. Genetic algorithms are used for finding the Gaussian process parameters.
Agnieszka Krok
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Gaussian Processes for Regression [PDF]
, 1995 The Bayesian analysis of neural networks is difficult because a sim ple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian anal ysis for fixed values of hyperparameters to be carried out exactly using matrix ...
Williams, Christopher, Rasmussen, Carl
openaire +3 more sources
Gaussian process dynamic programming [PDF]
Neurocomputing, 2009 Reinforcement learning (RL) and optimal control of systems with continuous states and actions require approximation techniques in most interesting cases. In this article, we introduce Gaussian process dynamic programming (GPDP), an approximate value function-based RL algorithm.
Marc Peter Deisenroth +2 more
openaire +3 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To clarify the clinical relevance of dopamine transporter single‐photon emission computed tomography (DAT‐SPECT) abnormalities in amyotrophic lateral sclerosis (ALS), with a prespecified focus on sex‐stratified associations with disease progression and short‐term prognosis.
Tomoya Kawazoe +7 more
wiley +1 more source
Leveraging Gaussian Processes in Remote Sensing
EnergiesPower grid reliability is crucial to supporting critical infrastructure, but monitoring and maintenance activities are expensive and sometimes dangerous.
Emma Foley
doaj +1 more source
Large deviations for conditionally Gaussian processes: estimates of level crossing probability
, 2018 The problem of (pathwise) large deviations for conditionally continuous Gaussian processes is investigated. The theory of large deviations for Gaussian processes is extended to the wider class of random processes -- the conditionally Gaussian processes ...
Pacchiarotti, Barbara +1 more
core +1 more source
Thermoreflectance Detection of Point Defects Resulting from Focused Ion Beam Milling
Advanced Engineering Materials, EarlyView.Focused ion beam (FIB) milling is a common tool for nanoscale material processing, however irradiation damage, redeposition, and contamination can occur. We use several characterization tools to show FIB‐induced effects beyond 1 mm from the milled area.
Thomas W. Pfeifer +3 more
wiley +1 more source