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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 process models. We test these methods on a realistic PWR assembly with gadolinium-added fuel rods, comparing them with non-adaptive supports. Results are promising, as the sampling algorithms allow to significantly reduce the size of interpolation supports with almost preserved accuracy. However, they exhibit phenomena of instability and stagnation, which calls for further investigation of the sampling dynamics and trying other scoring functions for the selection of samples.

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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 accuracy, low memory footprint, fast predictions… We here present a new framework to tackle this task, based on multi-outputs gaussian processes (MOGP). This machine learning model enables us to interpolate HXS’s with improved accuracy compared to the current multilinear standard, using only a fraction of its training data – meaning that the amount of required precomputation is reduced by a factor of several dozens. It also necessitates an even smaller fraction of its storage requirements, preserves its reconstruction speed, and unlocks new functionalities such as adaptive sampling and facilitated uncertainty quantification. We demonstrate the efficiency of this approach on a rich test case reproducing the VERA benchmark, proving in particular its scalability to datasets of millions of HXS.

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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 fidelity data. Then the moment matching method is applied to approximate the marginal prior of conditional DGP with a GP. The obtained effective kernels are implicit functions of the lower-fidelity data, manifesting the expressivity contributed by distribution propagation within the hierarchy. The hyperparameters are learned via optimizing the approximate marginal likelihood. Experiments with synthetic and high dimensional data show comparable performance against other multi-fidelity regression methods, variational inference, and multi-output GP. We conclude that, with the low fidelity data and the hierarchical DGP structure, the effective kernel encodes the inductive bias for true function allowing the compositional freedom.

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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) allows us to find and quantify the relationships between different channels. Several MOGPs are trained from different conditions grouped in bi-class problems, and the discrimination is performed based on the likelihood score of the test signals against all the models. Finally, the mean likelihood is computed to predict the correspondence of new inputs with each class’s existing models. Results show that this framework allows us to model the EEG signals adequately using generative models and allows analyzing the relationships between channels of the EEG for a particular condition. At the same time, the set of trained MOGPs is well suited to discriminate new input data.

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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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