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Oscillating Gaussian processes [PDF]
Statistical Inference for Stochastic Processes, 2020 AbstractIn this article we introduce and study oscillating Gaussian processes defined by$$X_t = \alpha _+ Y_t \mathbf{1}_{Y_t >0} + \alpha _- Y_t\mathbf{1}_{Y_t<0}$$Xt=α+Yt1Yt>0+α-Yt1Yt<0, where$$\alpha _+,\alpha _->0$$α+,α->0are free parameters andYis either stationary or self-similar Gaussian process.
Ilmonen, Pauliina +2 more
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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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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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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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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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CoRR, 2020 We present a new framework for recycling independent variational approximations to Gaussian processes. The main contribution is the construction of variational ensembles given a dictionary of fitted Gaussian processes without revisiting any subset of observations. Our framework allows for regression, classification and heterogeneous tasks, i.e.
Moreno-Muñoz, P. +2 more
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CoRR, 2021 Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of models for meta-learning that map data sets directly to predictive stochastic processes. We provide a rigorous analysis of the standard maximum-likelihood objective used to train conditional NPs.
Wessel P. Bruinsma +4 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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Splitting Gaussian processes for computationally-efficient regression.
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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