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Artykuły w czasopismach na temat "Multi-output gaussian processes"
Caro, Victor, Jou-Hui Ho, Scarlet Witting i 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.
Pełny tekst źródłaIngram, Martin, Damjan Vukcevic i Nick Golding. "Multi‐output Gaussian processes for species distribution modelling". Methods in Ecology and Evolution 11, nr 12 (15.10.2020): 1587–98. http://dx.doi.org/10.1111/2041-210x.13496.
Pełny tekst źródłaRodrigues, Filipe, Kristian Henrickson i Francisco C. Pereira. "Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation". IEEE Transactions on Intelligent Transportation Systems 20, nr 2 (luty 2019): 594–603. http://dx.doi.org/10.1109/tits.2018.2817879.
Pełny tekst źródłaVasudevan, Shrihari, Arman Melkumyan i Steven Scheding. "Efficacy of Data Fusion Using Convolved Multi-Output Gaussian Processes". Journal of Data Science 13, nr 2 (8.04.2021): 341–68. http://dx.doi.org/10.6339/jds.201504_13(2).0007.
Pełny tekst źródłaTruffinet, Olivier, Karim Ammar, Jean-Philippe Argaud, Nicolas Gérard Castaing i 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.
Pełny tekst źródłaRamirez, Wilmer Ariza, Juš Kocijan, Zhi Quan Leong, Hung Duc Nguyen i Shantha Gamini Jayasinghe. "Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes". International Journal of Automation and Computing 18, nr 5 (13.07.2021): 681–93. http://dx.doi.org/10.1007/s11633-021-1308-x.
Pełny tekst źródłaTruffinet, Olivier, Karim Ammar, Jean-Philippe Argaud, Nicolas Gérard Castaing i 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.
Pełny tekst źródłaLu, Chi-Ken, i Patrick Shafto. "Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning". Entropy 23, nr 11 (20.11.2021): 1545. http://dx.doi.org/10.3390/e23111545.
Pełny tekst źródłaTorres-Valencia, Cristian, Álvaro Orozco, David Cárdenas-Peña, Andrés Álvarez-Meza i Mauricio Álvarez. "A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis". Applied Sciences 10, nr 19 (27.09.2020): 6765. http://dx.doi.org/10.3390/app10196765.
Pełny tekst źródłaBae, Joonho, i Jinkyoo Park. "Count-based change point detection via multi-output log-Gaussian Cox processes". IISE Transactions 52, nr 9 (11.11.2019): 998–1013. http://dx.doi.org/10.1080/24725854.2019.1676937.
Pełny tekst źródłaRozprawy doktorskie na temat "Multi-output gaussian processes"
Parra, Vásquez Gabriel Enrique. "Spectral mixture kernels for Multi-Output Gaussian processes". Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/150553.
Pełny tekst źródłaMulti-Output Gaussian Processes (MOGPs) are the multivariate extension of Gaussian processes (GPs \cite{Rasmussen:2006}), a Bayesian nonparametric method for univariate regression. MOGPs address the multi-channel regression problem by modeling the correlation in time and/or space (as scalar GPs do), but also across channels and thus revealing statistical dependencies among different sources of data. This is crucial in a number of real-world applications such as fault detection, data imputation and financial time-series analysis. Analogously to the univariate case, MOGPs are entirely determined by a multivariate covariance function, which in this case is matrix valued. The design of this matrix-valued covariance function is challenging, since we have to deal with the trade off between (i) choosing a broad class of cross-covariances and auto-covariances, while at the same time (ii) ensuring positive definiteness of the symmetric matrix containing these scalar-valued covariance functions. In the stationary univariate case, these difficulties can be bypassed by virtue of Bochner's theorem, that is, by building the covariance function in the spectral (Fourier) domain to then transform it to the time and/or space domain, thus yielding the (single-output) Spectral Mixture kernel \cite{Wilson:2013}. A classical approach to define multivariate covariance functions for MOGPs is through linear combinations of independent (latent) GPs; this is the case of the Linear Model of Coregionalization (LMC \cite{goo1997}) and the Convolution Model \cite{Alvarez:2008}. In these cases, the resulting multivariate covariance function is a function of both the latent-GP covariances and the linear operator considered, which usually results in symmetric cross-covariances that do not admit lags across channels. Due to their simplicity, these approaches fail to provide interpretability of the dependencies learnt and force the auto-covariances to have similar structure. The main purpose of this work is to extend the spectral mixture concept to MOGPs: We rely on Cram\'er's theorem \cite, the multivariate version of Bochner's theorem, to propose an expressive family of complex-valued square-exponential cross-spectral densities, which, through the Fourier transform yields the Multi-Output Spectral Mixture kernel (MOSM). The proposed MOSM model provides clear interpretation of all the parameters in spectral terms. Besides the theoretical presentation and interpretation of the proposed multi-output covariance kernel based on square-exponential spectral densities, we inquiry the plausibility of complex-valued t-Student cross-spectral densities. We validate our contribution experimentally through an illustrative example using a tri-variate synthetic signal, and then compare it against all the aforementioned methods on two real-world datasets.
Malik, Obaid. "Probabilistic leak detection and quantification using multi-output Gaussian processes". Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/409717/.
Pełny tekst źródłaTruffinet, Olivier. "Machine learning methods for cross-section reconstruction in full-core deterministic neutronics codes". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP128.
Pełny tekst źródłaToday, most deterministic neutronics simulators for nuclear reactors follow a two-step multi-scale scheme. In a so-called “lattice” calculation, the physics is finely resolved at the level of the elementary reactor pattern (fuel assemblies); these tiles are then brought into contact in a so-called “core” calculation, where the overall configuration is calculated more coarsely. Communication between these two codes is realized by the deferred transfer of physical data, the most important of which are called “homogenized cross sections” (hereafter referred to as HXS) and can be represented by multivariate functions. Their deferred use and dependence on variable physical conditions call for a tabulation-interpolation scheme: HXS are precalculated in a wide range of situations, stored, then approximated in the core code from the stored values to correspond to a specific reactor state. In a context of increasing simulation finesse, the mathematical tools currently used for this approximation stage are now showing their limitations. The aim of this thesis is to find replacements for them, capable of making HXS interpolation more accurate, more economical in terms of data and storage space, and just as fast. The whole arsenal of machine learning, functional approximation, etc., can be put at use to tackle this problem.In order to find a suitable approximation model, we began by analyzing the datasets generated for this thesis: correlations between HXS's, shapes of their dependencies, linear dimension, etc. This last point proved particularly fruitful: HXS sets turn out to be of very low effective dimension, which greatly simplifies their approximation. In particular, we leveraged this fact to develop an innovative methodology based on the Empirical Interpolation Method (EIM), capable of replacing the majority of lattice code calls by extrapolations from a small volume of data, and reducing HXS storage by one or two orders of magnitude - all with a negligible loss of accuracy.To retain the advantages of such a methodology while addressing the full scope of the thesis problem, we then turned to a powerful machine learning model matching the same low-dimensional structure: multi-output Gaussian processes (MOGPs). Proceeding step by step from the simplest Gaussian models (single-output GPs) to most complex ones, we showed that these tools are fully adapted to the problem under consideration, and offer major gains over current HXS interpolation routines. Numerous modeling choices were discussed and compared; models were adapted to very large data, requiring some optimization of their implementation; and the new functionalities which they offer were tested, notably uncertainty prediction and active learning.Finally, theoretical work was carried out on the studied family of models - the Linear Model of Co-regionalisation (LMC) - in order to shed light on certain grey areas in their still young theory. This led to the definition of a new model, the PLMC, which was implemented, optimized and tested on numerous real and synthetic data sets. Simpler than its competitors, this model has also proved to be just as accurate and fast if not more so, and holds a number of exclusive functionalities that were put to good use during the thesis.This work opens up many new prospects for neutronics simulation. Equipped with powerful and flexible learning models, it is possible to envisage significant evolutions for deterministic codes: systematic propagation of uncertainties, correction of various approximations, taking into account of more variables
Vestin, Albin, i Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms". Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
Pełny tekst źródłaCzęści książek na temat "Multi-output gaussian processes"
Cardona, Hernán Darío Vargas, Mauricio A. Álvarez i Álvaro A. Orozco. "Convolved Multi-output Gaussian Processes for Semi-Supervised Learning". W Image Analysis and Processing — ICIAP 2015, 109–18. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_10.
Pełny tekst źródłaLui, Sin Ting, Thierry Peynot, Robert Fitch i Salah Sukkarieh. "Enhanced Stochastic Mobility Prediction on Unstructured Terrain Using Multi-output Gaussian Processes". W Intelligent Autonomous Systems 13, 173–90. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08338-4_14.
Pełny tekst źródłaCuellar-Fierro, Jhon F., Hernán Darío Vargas-Cardona, Mauricio A. Álvarez, Andrés M. Álvarez i Álvaro A. Orozco. "Non-stationary Multi-output Gaussian Processes for Enhancing Resolution over Diffusion Tensor Fields". W Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 168–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75193-1_21.
Pełny tekst źródłaJ., Jayapradha, Lakshmi Vadhanie, Yukta Kulkarni, T. Senthil Kumar i Uma Devi M. "Enhancing Algorithmic Resilience Against Data Poisoning Using CNN". W Risk Assessment and Countermeasures for Cybersecurity, 131–57. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2691-6.ch008.
Pełny tekst źródłaSimeone, Davide, Marta Lenatti, Constantino Lagoa, Karim Keshavjee, Aziz Guergachi, Fabrizio Dabbene i Alessia Paglialonga. "Multi-Input Multi-Output Dynamic Modelling of Type 2 Diabetes Progression". W Telehealth Ecosystems in Practice. IOS Press, 2023. http://dx.doi.org/10.3233/shti230784.
Pełny tekst źródłaStreszczenia konferencji na temat "Multi-output gaussian processes"
Lim, Jaehyun, Jehyun Park, Sungjae Nah i Jongeun Choi. "Multi-output Infinite Horizon Gaussian Processes". W 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561031.
Pełny tekst źródłaDario Vargas Cardona, Hernan, Alvaro A. Orozco i Mauricio A. Alvarez. "Multi-output Gaussian processes for enhancing resolution of diffusion tensor fields". W 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2016. http://dx.doi.org/10.1109/embc.2016.7590898.
Pełny tekst źródłaMateo-Sanchis, Anna, Jordi Munoz-Mari, Manuel Campos-Taberner, Javier Garcia-Haro i Gustau Camps-Valls. "Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes". W IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8519254.
Pełny tekst źródłaGaneva, Dessislava, Milen Chanev, Darina Valcheva, Lachezar Filchev i Georgi Jelev. "MODELLING BARLEY BIOMASS FROM PHENOCAM TIME SERIES WITH MULTI-OUTPUT GAUSSIAN PROCESSES". W 22nd SGEM International Multidisciplinary Scientific GeoConference 2022. STEF92 Technology, 2022. http://dx.doi.org/10.5593/sgem2022/2.1/s08.15.
Pełny tekst źródłaChiplunkar, Ankit, Emmanuel Rachelson, Michele Colombo i Joseph Morlier. "Adding Flight Mechanics to Flight Loads Surrogate Model using Multi-Output Gaussian Processes". W 17th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-4000.
Pełny tekst źródłaGhasempour, Alireza, i Manel Martínez-Ramón. "Short-Term Electric Load Prediction in Smart Grid using Multi-Output Gaussian Processes Regression". W 2023 IEEE Kansas Power and Energy Conference (KPEC). IEEE, 2023. http://dx.doi.org/10.1109/kpec58008.2023.10215490.
Pełny tekst źródłaOsborne, M. A., S. J. Roberts, A. Rogers, S. D. Ramchurn i N. R. Jennings. "Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-output Gaussian Processes". W 2008 7th International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 2008. http://dx.doi.org/10.1109/ipsn.2008.25.
Pełny tekst źródłaAali, Mohammad, i Jun Liu. "Learning Piecewise Residuals of Control Barrier Functions for Safety of Switching Systems using Multi-Output Gaussian Processes". W 2024 European Control Conference (ECC). IEEE, 2024. http://dx.doi.org/10.23919/ecc64448.2024.10591208.
Pełny tekst źródłaGeroulas, Vasileios, Zissimos P. Mourelatos, Vasiliki Tsianika i Igor Baseski. "Reliability of Nonlinear Vibratory Systems Under Non-Gaussian Loads". W ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67313.
Pełny tekst źródłaWang, Liwei, Suraj Yerramilli, Akshay Iyer, Daniel Apley, Ping Zhu i Wei Chen. "Data-Driven Design via Scalable Gaussian Processes for Multi-Response Big Data With Qualitative Factors". W ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71570.
Pełny tekst źródłaRaporty organizacyjne na temat "Multi-output gaussian processes"
Bilionis, Ilias, i Nicholas Zabaras. Multi-output Local Gaussian Process Regression: Applications to Uncertainty Quantification. Fort Belvoir, VA: Defense Technical Information Center, grudzień 2011. http://dx.doi.org/10.21236/ada554929.
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