Results 21 to 30 of about 731,480 (301)
Projection pursuit Gaussian process regression
IISE Transactions, 2022 A primary goal of computer experiments is to reconstruct the function given by the computer code via scattered evaluations. Traditional isotropic Gaussian process models suffer from the curse of dimensionality, when the input dimension is relatively high given limited data points. Gaussian process models with additive correlation functions are scalable
Gecheng Chen, Rui Tuo
openaire +2 more sources
Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network
Energies, 2022 Data play an essential role in the optimal control of smart buildings’ operation, especially in building energy-management for the target of nearly zero buildings.
Anh Tuan Phan +5 more
doaj +1 more source
Stein's method of exchangeable pairs in multivariate functional approximations [PDF]
, 2021 In this paper we develop a framework for multivariate functional approximation by a suitable Gaussian process via an exchangeable pairs coupling that satisfies a suitable approximate linear regression property, thereby building on work by Barbour (1990 ...
Döbler, Christian +1 more
core +2 more sources
Reconstructing QCD spectral functions with Gaussian processes [PDF]
Physical Review D, 2021 We reconstruct ghost and gluon spectral functions in 2+1 flavor QCD with Gaussian process regression. This framework allows us to largely suppress spurious oscillations and other common reconstruction artifacts by specifying generic magnitude and length ...
J. Horák +6 more
semanticscholar +1 more source
Large-Scale Heteroscedastic Regression via Gaussian Process [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 14 pages, 15 ...
Haitao Liu, Yew-Soon Ong, Jianfei Cai
openaire +4 more sources
Prediction of operating dynamics in floating-zone crystal growth using Gaussian mixture model
Science and Technology of Advanced Materials: Methods, 2022 We have applied a Gaussian mixture regression to the prediction of operation dynamics in floating zone crystal growth as an example of a materials process.
R. Omae, S. Sumitani, Y. Tosa, S. Harada
doaj +1 more source
Quantum Gaussian process regression for Bayesian optimization [PDF]
Quantum Machine Intelligence, 2023 Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on parameterized quantum circuits.
Frederic Rapp, Marco Roth
semanticscholar +1 more source
Sparse multiscale gaussian process regression [PDF]
Proceedings of the 25th international conference on Machine learning - ICML '08, 2008 Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of the g.p. with one of its two inputs fixed. We generalise this for the case of Gaussian covariance function, by basing our computations on m Gaussian basis functions with ...
Walder, Christian +2 more
openaire +3 more sources
The pitfalls of using Gaussian Process Regression for normative modeling.
PLoS ONE, 2021 Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity.
Bohan Xu +3 more
doaj +1 more source
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. Standard GP regression typically results in a proxy model which is unbounded for all temporal or spacial points, and thus leaves the possibility of taking on ...
Andrew Pensoneault, Xiu Yang, Xueyu Zhu
openaire +3 more sources