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PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
In this paper, we present a novel surrogate model for representation learning and uncertainty quantification, which aims to deal with data of moderate to high dimensions.
Gauss-Legendre Features for Gaussian Process Regression
Our method is very much inspired by the well-known random Fourier features approach, which also builds low-rank approximations via numerical integration.
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