Results 11 to 20 of about 195,711 (268)
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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Gaussian Processes and Gaussian Measures
The Annals of Mathematical Statistics, 1972 The subject of this paper is the study of the correspondence between Gaussian processes with paths in linear function spaces and Gaussian measures on function spaces. For the function spaces $C(I), C^n\lbrack a, b\rbrack, AC\lbrack a, b\rbrack$ and $L_2(T, \mathscr{A}, \nu)$ it is shown that if a Gaussian process has paths in these spaces then it ...
Rajput, Balram S., Cambanis, Stamatis
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Recurrent Gaussian processes [PDF]
, 2015 Published as a conference paper at ICLR 2016.
Mattos, C.L.C. +5 more
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CONVEX BODIES AND GAUSSIAN PROCESSES
Image Analysis and Stereology, 2011 For several decades, the topics of the title have had a fruitful interaction. This survey will describe some of these connections, including the GB/GC classification of convex bodies, Ito-Nisio singularities from a geometric viewpoint, Gaussian ...
Richard A Vitale
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Gaussian process hydrodynamics
Applied Mathematics and Mechanics, 2023 AbstractWe present a Gaussian process (GP) approach, called Gaussian process hydrodynamics (GPH) for approximating the solution to the Euler and Navier-Stokes (NS) equations. Similar to smoothed particle hydrodynamics (SPH), GPH is a Lagrangian particle-based approach that involves the tracking of a finite number of particles transported by a flow ...
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Continuity of Gaussian Processes [PDF]
The Annals of Probability, 1986 The author first gives a generalization of \textit{M. B. Marcus} and \textit{L. A. Shepp}'s [Proc. Sixth Berkeley Sympos. math. Statist. Probab., Univ. Calif. 1970, 2, 423-441 (1972; Zbl 0379.60040)] theorem on the equivalence between sample continuity of a Gaussian process defined on a compact subset of a metric space, and a.s.
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HYPERPARAMETER OPTIMIZATION BASED ON A PRIORI AND A POSTERIORI KNOWLEDGE ABOUT CLASSIFICATION PROBLEM [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2020 Subject of Research. The paper deals with Bayesian method for hyperparameter optimization of algorithms, used in machine learning for classification problems.
Valentina S. Smirnova +3 more
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J. Mach. Learn. Res., 2020 We introduce a novel way to combine boosting with Gaussian process and mixed effects models. This allows for relaxing, first, the zero or linearity assumption for the prior mean function in Gaussian process and grouped random effects models in a flexible non-parametric way and, second, the independence assumption made in most boosting algorithms.
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Modern Stochastics: Theory and Applications, 2015 We show that every multiparameter Gaussian process with integrable variance function admits a Wiener integral representation of Fredholm type with respect to the Brownian sheet.
Tommi Sottinen, Lauri Viitasaari
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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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