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pro vyhledávání: '"Ouyang, Ruofei"'
This paper presents a novel decentralized high-dimensional Bayesian optimization (DEC-HBO) algorithm that, in contrast to existing HBO algorithms, can exploit the interdependent effects of various input components on the output of the unknown objecti
Externí odkaz:
http://arxiv.org/abs/1711.07033
Autor:
Ouyang, Ruofei, Low, Kian Hsiang
This paper presents novel Gaussian process decentralized data fusion algorithms exploiting the notion of agent-centric support sets for distributed cooperative perception of large-scale environmental phenomena. To overcome the limitations of scale in
Externí odkaz:
http://arxiv.org/abs/1711.06064
Autor:
Chen, Jie, Cao, Nannan, Low, Kian Hsiang, Ouyang, Ruofei, Tan, Colin Keng-Yan, Jaillet, Patrick
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due to its cubic time cost in the data size. This pape
Externí odkaz:
http://arxiv.org/abs/1408.2060
Autor:
Chen, Jie, Cao, Nannan, Low, Kian Hsiang, Ouyang, Ruofei, Tan, Colin Keng-Yan, Jaillet, Patrick
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-time predictions due to its cubic time cost in the data size. This pape
Externí odkaz:
http://arxiv.org/abs/1305.5826
Akademický článek
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Publikováno v:
MIT web domain
A key challenge of environmental sensing and monitoring is that of sensing, modeling, and predicting large-scale, spatially correlated environmental phenomena, especially when they are unknown and non-stationary. This paper presents a decentralized m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od________88::84449297d17042ed1ddc37b9b04557f1
https://orcid.org/0000-0002-8585-6566
https://orcid.org/0000-0002-8585-6566