Results 241 to 250 of about 300,432 (267)
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IEEE Transactions on Neural Networks, 2011
Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of ...
Sotirios P. Chatzis, Yiannis Demiris
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Echo state networks (ESNs) constitute a novel approach to recurrent neural network (RNN) training, with an RNN (the reservoir) being generated randomly, and only a readout being trained using a simple computationally efficient algorithm. ESNs have greatly facilitated the practical application of RNNs, outperforming classical approaches on a number of ...
Sotirios P. Chatzis, Yiannis Demiris
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Gaussian Processes and Neuronal Modeling
Natural Computing, 2005zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Elvira Di Nardo +3 more
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On Gaussian Markov processes and Polya processes
Operations Research LetterszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kerry W. Fendick, Ward Whitt
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2004
We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning algorithm chooses a nonlinear transformation such that transformed data is well-modelled by a GP.
Snelson, E. +2 more
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We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning algorithm chooses a nonlinear transformation such that transformed data is well-modelled by a GP.
Snelson, E. +2 more
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Gaussian Variables and Gaussian Processes
2016Gaussian random processes play an important role both in theoretical probability and in various applied models. We start by recalling basic facts about Gaussian random variables and Gaussian vectors. We then discuss Gaussian spaces and Gaussian processes, and we establish the fundamental properties concerning independence and conditioning in the ...
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An Intuitive Tutorial to Gaussian Process Regression
Computing in Science and Engineering, 2023Jie Wang
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Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model
Water Research, 2022Omid Chatrabgoun +2 more
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Asymmetric Gaussian Process multi-view learning for visual classification
Information Fusion, 2021Jinxing Li, David Zhang, Yong Xu
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A hybrid Gaussian process model for system reliability analysis
Reliability Engineering and System Safety, 2020Chao Hu +2 more
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Scalable Gaussian Process Classification With Additive Noise for Non-Gaussian Likelihoods
IEEE Transactions on Cybernetics, 2022Xiaobo Shen, Haitao Liu, Yew-Soon Ong
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