4 papers
Deep Jump Gaussian Processes for Surrogate Modeling of High-Dimensional Piecewise Continuous Functions
Yang Xu, Chiwoo Park
We introduce Deep Jump Gaussian Processes (DJGP), a novel method for surrogate modeling of a piecewise continuous function on a high-dimensional domain. DJGP addresses the limitati…
Structure-Preserving Margin Distribution Learning for High-Order Tensor Data with Low-Rank Decomposition
Yang Xu, Junpeng Li, Changchun Hua +1
The Large Margin Distribution Machine (LMDM) is a recent advancement in classifier design that optimizes not just the minimum margin (as in SVM) but the entire margin distribution,…
Deep regularization networks for inverse problems with noisy operators
Fatemeh Pourahmadian, Yang Xu
A supervised learning approach is proposed for regularization of large inverse problems where the main operator is built from noisy data. This is germane to superresolution imaging…
Network scaling and scale-driven loss balancing for intelligent poroelastography
Yang Xu, Fatemeh Pourahmadian
A deep learning framework is developed for multiscale characterization of poroelastic media from full waveform data which is known as poroelastography. Special attention is paid to…