Results 31 to 40 of about 196,185 (268)
Gaussian processes for computer experiments
ESAIM: Proceedings and Surveys, 2017 This paper collects the contributions which were presented during the session devoted to Gaussian processes at the Journées MAS 2016. First, an introduction to Gaussian processes is provided, and some current research questions are discussed.
Bachoc François +3 more
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Design Space Exploration of Turbulent Multiphase Flows Using Machine Learning-Based Surrogate Model
Energies, 2020 This study focuses on establishing a surrogate model based on machine learning techniques to predict the time-averaged spatially distributed behaviors of vaporizing liquid jets in turbulent air crossflow for momentum flux ratios between 5 and 120.
Himakar Ganti +2 more
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CoRR, 2012 In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We
Damianou, A.C., Lawrence, N.D.
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Gaussian Process for Trajectories
, 2023 The Gaussian process is a powerful and flexible technique for interpolating spatiotemporal data, especially with its ability to capture complex trends and uncertainty from the input signal. This chapter describes Gaussian processes as an interpolation technique for geospatial trajectories.
Kien Nguyen 0003 +2 more
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Skew Gaussian processes for classification [PDF]
Machine Learning, 2020 AbstractGaussian processes (GPs) are distributions over functions, which provide a Bayesian nonparametric approach to regression and classification. In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model. This implies, for instance, that
Alessio Benavoli +2 more
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A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis
Applied Sciences, 2020 The study of brain electrical activity (BEA) from different cognitive conditions has attracted a lot of interest in the last decade due to the high number of possible applications that could be generated from it.
Cristian Torres-Valencia +4 more
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Gaussian process deconvolution
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2023 Let us consider the deconvolution problem, i.e. to recover a latent source x ( ⋅ )
Felipe Tobar +2 more
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Gaussian Processes for Blazar Variability Studies
Galaxies, 2017 This article briefly introduces Gaussian processes as a new approach for modelling time series in the field of blazar physics. In the second part of the paper, recent results from an application of GP modelling to the multi-wavelength light curves of the
Vassilis Karamanavis
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CoRR, 2011 Appearing in Neural Information Processing Systems ...
Duvenaud, D. +2 more
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On the Degeneracy between fσ8 Tension and Its Gaussian Process Forecasting
Universe, 2022 In this Article, we reconstruct the growth and evolution of the cosmic structure of the Universe using Markov chain Monte Carlo algorithms for Gaussian processes.
Mauricio Reyes, Celia Escamilla-Rivera
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