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Thematische Bibliographien / Multi-output gaussian processes

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Auswahl der wissenschaftlichen Literatur zum Thema „Multi-output gaussian processes“

Autor: Grafiati

Veröffentlicht am 11. Januar 2025

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Zeitschriftenartikel zum Thema "Multi-output gaussian processes"

1

Caro, Victor, Jou-Hui Ho, Scarlet Witting, and Felipe Tobar. "Modeling Neonatal EEG Using Multi-Output Gaussian Processes." IEEE Access 10 (2022): 32912–27. http://dx.doi.org/10.1109/access.2022.3159653.

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2

Ingram, Martin, Damjan Vukcevic, and Nick Golding. "Multi‐output Gaussian processes for species distribution modelling." Methods in Ecology and Evolution 11, no. 12 (2020): 1587–98. http://dx.doi.org/10.1111/2041-210x.13496.

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3

Rodrigues, Filipe, Kristian Henrickson, and Francisco C. Pereira. "Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation." IEEE Transactions on Intelligent Transportation Systems 20, no. 2 (2019): 594–603. http://dx.doi.org/10.1109/tits.2018.2817879.

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4

Vasudevan, Shrihari, Arman Melkumyan, and Steven Scheding. "Efficacy of Data Fusion Using Convolved Multi-Output Gaussian Processes." Journal of Data Science 13, no. 2 (2021): 341–68. http://dx.doi.org/10.6339/jds.201504_13(2).0007.

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5

Truffinet, Olivier, Karim Ammar, Jean-Philippe Argaud, Nicolas Gérard Castaing, and Bertrand Bouriquet. "Adaptive sampling of homogenized cross-sections with multi-output gaussian processes." EPJ Web of Conferences 302 (2024): 02010. http://dx.doi.org/10.1051/epjconf/202430202010.

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In another talk submitted to this conference, we presented an efficient new framework based on multi-outputs gaussian processes (MOGP) for the interpolation of few-groups homogenized cross-sections (HXS) inside deterministic core simulators. We indicated that this methodology authorized a principled selection of interpolation points through adaptive sampling. We here develop this idea by trying simple sampling schemes on our problem. In particular, we compare sample scoring functions with and without integration of leave-one-out errors, and obtained with single-output and multi-output gaussian

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6

Ramirez, Wilmer Ariza, Juš Kocijan, Zhi Quan Leong, Hung Duc Nguyen, and Shantha Gamini Jayasinghe. "Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes." International Journal of Automation and Computing 18, no. 5 (2021): 681–93. http://dx.doi.org/10.1007/s11633-021-1308-x.

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7

Truffinet, Olivier, Karim Ammar, Jean-Philippe Argaud, Nicolas Gérard Castaing, and Bertrand Bouriquet. "Multi-output gaussian processes for the reconstruction of homogenized cross-sections." EPJ Web of Conferences 302 (2024): 02006. http://dx.doi.org/10.1051/epjconf/202430202006.

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Deterministic nuclear reactor simulators employing the prevalent two-step scheme often generate a substantial amount of intermediate data at the interface of their two subcodes, which can impede the overall performance of the software. The bulk of this data comprises “few-groups homogenized cross-sections” or HXS, which are stored as tabulated multivariate functions and interpolated inside the core simulator. A number of mathematical tools have been studied for this interpolation purpose over the years, but few meet all the challenging requirements of neutronics computation chains: extreme acc

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8

Lu, Chi-Ken, and Patrick Shafto. "Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning." Entropy 23, no. 11 (2021): 1545. http://dx.doi.org/10.3390/e23111545.

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Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, it was pointed out that the hierarchical structure of DGP well suited modeling the multi-fidelity regression, in which one is provided sparse observations with high precision and plenty of low fidelity observations. We propose the conditional DGP model in which the latent GPs are directly supported by the fixed lower

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9

Torres-Valencia, Cristian, Álvaro Orozco, David Cárdenas-Peña, Andrés Álvarez-Meza, and Mauricio Álvarez. "A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis." Applied Sciences 10, no. 19 (2020): 6765. http://dx.doi.org/10.3390/app10196765.

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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. In this work, a discriminative framework for BEA via electroencephalography (EEG) is proposed based on multi-output Gaussian Processes (MOGPs) with a specialized spectral kernel. First, a signal segmentation stage is executed, and the channels from the EEG are used as the model outputs. Then, a novel covariance function within the MOGP known as the multispectral mixture kernel (MOSM)

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10

Bae, Joonho, and Jinkyoo Park. "Count-based change point detection via multi-output log-Gaussian Cox processes." IISE Transactions 52, no. 9 (2019): 998–1013. http://dx.doi.org/10.1080/24725854.2019.1676937.

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