Results 1 to 10 of about 110,828 (307)
Nonnegativity-enforced Gaussian process regression
Theoretical and Applied Mechanics Letters, 2020 : Gaussian process (GP) regression is a flexible non-parametric approach to approximate complex models. In many cases, these models correspond to processes with bounded physical properties.
Andrew Pensoneault, Xiu Yang, Xueyu Zhu
doaj +5 more sources
An Intuitive Tutorial to Gaussian Process Regression
Computing in Science and Engineering, 2023 8 pages, 12 ...
Jie Wang
exaly +3 more sources
Efficient inference of synaptic plasticity rule with Gaussian process regression [PDF]
iScience, 2023 Summary: Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings.
Shirui Chen, Qixin Yang, Sukbin Lim
doaj +2 more sources
Efficiency improvement of spin-resolved ARPES experiments using Gaussian process regression [PDF]
Scientific ReportsThe experimental efficiency has been a central concern for time-consuming experiments. Spin- and angle-resolved photoemission spectroscopy (spin-resolved ARPES) is renowned for its inefficiency in spin-detection, despite its outstanding capability to ...
Hideaki Iwasawa +8 more
doaj +2 more sources
Modeling forecast errors for microgrid operation using Gaussian process regression [PDF]
Scientific ReportsMicrogrids, denoting small-scale and self-sustaining grids, constitute a pivotal component in future power systems with a high penetration of renewable generators.
Yeuntae Yoo, Seungmin Jung
doaj +2 more sources
Lossy compression of observations for Gaussian process regression [PDF]
MATEC Web of Conferences, 2022 This paper proposes a novel approach of Gaussian process observation set compression based on a squared difference measure. It is used to discard observations to speed up Gaussian process prediction while retaining the information encoded in the full set
Visser Emile +2 more
doaj +1 more source
Sustainable Dyeing Process Modeling for Recycled PET/PCT Microfibers via Gaussian Process Regression [PDF]
ACS OmegaHyeokjun Cho, Seung Geol Lee
doaj +2 more sources
Non-Gaussian Process Regression
CoRR, 2022 Standard GPs offer a flexible modelling tool for well-behaved processes. However, deviations from Gaussianity are expected to appear in real world datasets, with structural outliers and shocks routinely observed. In these cases GPs can fail to model uncertainty adequately and may over-smooth inferences.
Yaman Kindap, Simon J. Godsill
openaire +2 more sources
Manifold Gaussian Processes for regression [PDF]
2016 International Joint Conference on Neural Networks (IJCNN), 2016 26.03.14 KB.
Roberto Calandra +3 more
openaire +5 more sources
Tunnel geomechanical parameters prediction using Gaussian process regression
Machine Learning with Applications, 2021 The purpose of this study is to apply a modern intelligent method of Gaussian process regression (GPR) to predict the geological parameter of Rock Quality Designation (RQD) along the tunnel route. This method can also be used for any geological parameter
Arsalan Mahmoodzadeh +6 more
doaj +1 more source