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23 hours ago · We present a novel active learning strategy to adaptively learn multifidelity Gaussian process models leveraging the leave-one-out cross-validation (LOO-CV) ...
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21 hours ago · A novel probabilistic back-analysis framework based on matrix-variate deep Gaussian process (MVDGP), Nadam and Bayesian theory provides a significant method for ...
5 hours ago · Bayesian-based algorithms, such as Gaussian process regression (GPR), describe problems probabilistically and can provide prediction intervals to address ...
21 hours ago · For example, Wang et al. (2021a) applies this method to the Gaussian Process model by adding information as constraints. Another work that uses such surrogate ...
21 hours ago · (42) Expands on basic Gaussian process techniques for few-shot learning by modeling the posterior distribution with an ODE-based normalizing flow. MetaNet. (40) ...
20 hours ago · This study addresses the challenge of identifying anomalies within multivariate time series data, focusing specifically on the operational parameters of gas ...
10 hours ago · The method combines a machine learning approach (i.e., an analog forecasting method) ... To go beyond our Gaussian approach, a non-parametric assimilation method ...
12 hours ago · A novel validation metric is proposed first within the Bayesian theory by using the normalized half-power bandwidth frequency transformation (NHBFT) and the ...
16 hours ago · In this paper, we propose an uncertainty-aware, learning-based approach to ... Gaussian processes (GPs) based on real-time measurements of the environment.