Results 41 to 50 of about 731,480 (301)
Infinite Mixtures of Multivariate Gaussian Processes [PDF]
, 2013 This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning.
Sun, Shiliang
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Engineering Applications of Computational Fluid Mechanics, 2021 Among several indicators for river engineering sustainability, the longitudinal dispersion coefficient ( $ K_x $ ) is the main parameter that defines the transport of pollutants in natural streams.
Leonardo Goliatt +4 more
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Gaussian processes for sound field reconstruction.
Journal of the Acoustical Society of America, 2021 This study examines the use of Gaussian process (GP) regression for sound field reconstruction. GPs enable the reconstruction of a sound field from a limited set of observations based on the use of a covariance function (a kernel) that models the spatial
Diego Caviedes-Nozal +5 more
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Quantum Gaussian process regression
, 2021 In this paper, a quantum algorithm based on gaussian process regression model is proposed. The proposed quantum algorithm consists of three sub-algorithms. One is the first quantum subalgorithm to efficiently generate mean predictor. The improved HHL algorithm is proposed to obtain the sign of outcomes. Therefore, the terrible situation that results is
Chen, Menghan +3 more
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Heteroscedastic Gaussian process regression [PDF]
Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005 This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance locally unlike standard Gaussian Process regression or SVMs. This means that our estimator adapts to the local noise. The problem is cast in the setting of maximum a posteriori
Quoc V. Le +2 more
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Non-Gaussian Process Regression
, 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.
Kındap, Yaman, Godsill, Simon
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Quantum-assisted Gaussian process regression
Physical Review A, 2019 Gaussian processes (GP) are a widely used model for regression problems in supervised machine learning. Implementation of GP regression typically requires $O(n^3)$ logic gates. We show that the quantum linear systems algorithm [Harrow et al., Phys. Rev. Lett.
Zhao, Zhikuan +2 more
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Gaussian Semiparametric Estimation of Non-stationary Time Series [PDF]
, 1998 Generalizing the definition of the memory parameter d in terms of the differentiated series, we showed in Velasco (Non-stationary log-periodogram regression, Forthcoming J.
Velasco, Carlos
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Prediction of concrete corrosion in sewers with hybrid Gaussian processes regression model
, 2017 Concrete corrosion is a major concern for sewer authorities due to the significantly shortened service life, which is governed by the corrosion rate and the corrosion initiation time. This paper proposes a hybrid Gaussian Processes Regression (GPR) model
Yiqi Liu +4 more
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Regression with Gaussian Processes [PDF]
, 1997 The Bayesian analysis of neural networks is difficult because the prior over functions has a complex form, leading to implementations that either make approximations or use Monte Carlo integration techniques. In this paper I investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian analysis to be carried out ...
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