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Oscillating Gaussian Processes [PDF]
Statistical Inference for Stochastic Processes, 2019 In this article we introduce and study oscillating Gaussian processes defined by $X_t = \alpha_+ Y_t {\bf 1}_{Y_t >0} + \alpha_- Y_t{\bf 1}_{Y_t0$ are free parameters and $Y$ is either stationary or self-similar Gaussian process.
Ilmonen, Pauliina +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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Splitting Gaussian processes for computationally-efficient regression. [PDF]
PLoS ONE, 2021 Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer from poor scaling in the number of observations.
Nick Terry, Youngjun Choe
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Nonlinear Multiscale Modelling and Design using Gaussian Processes [PDF]
Journal of Applied and Computational Mechanics, 2021 A method for nonlinear material modeling and design using statistical learning is proposed to assist in the mechanical analysis of structural materials. Conventional computational homogenization schemes are proven to underperform in analyzing the complex
Sumudu Herath, Udith Haputhanthri
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Detecting periodicities with Gaussian processes [PDF]
PeerJ Computer Science, 2016 We consider the problem of detecting and quantifying the periodic component of a function given noise-corrupted observations of a limited number of input/output tuples.
Nicolas Durrande +3 more
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Bayesian eikonal tomography using Gaussian processes
Seismica, 2023 Eikonal tomography has become a popular methodology for deriving phase velocity maps from surface wave phase delay measurements. Its high efficiency makes it popular for handling datasets deriving from large-N arrays, in particular in the ambient-noise
Jack Muir
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Classification of cassava leaf diseases using deep Gaussian transfer learning model
Engineering Reports, 2023 In Sub‐Saharan Africa, experts visually examine the plants and look for disease symptoms on the leaves to diagnose cassava diseases, a subjective method. Machine learning algorithms have been employed to quickly identify and classify crop diseases.
Ahishakiye Emmanuel +4 more
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Stokesian processes : inferring Stokes flows using physics-informed Gaussian processes
Machine Learning: Science and Technology, 2023 We develop a probabilistic Stokes flow framework, using physics informed Gaussian processes, which can be used to solve both forward/inverse flow problems with missing and/or noisy data.
John J Molina +2 more
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Enhanced Checkerboard Detection Using Gaussian Processes
Mathematics, 2023 Accurate checkerboard detection is of vital importance for computer vision applications, and a variety of checkerboard detectors have been developed in the past decades.
Michaël Hillen +8 more
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Sparse Gaussian Processes on Discrete Domains
IEEE Access, 2021 Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data.
Vincent Fortuin +3 more
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