3 citations · 3 across the 4 of their papers we have counts for
4 papers
Principal Feature Detection via -Sobolev Inequalities
Matthew T. C. Li, Youssef Marzouk, Olivier Zahm
We investigate the approximation of high-dimensional target measures as low-dimensional updates of a dominating reference measure. This approximation class replaces the associated…
Redatuming physical systems using symmetric autoencoders
Pawan Bharadwaj, Matthew Li, Laurent Demanet
This paper considers physical systems described by hidden states and indirectly observed through repeated measurements corrupted by unmodeled nuisance parameters. A network-based r…
Accurate and Robust Deep Learning Framework for Solving Wave-Based Inverse Problems in the Super-Resolution Regime
Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez
We propose an end-to-end deep learning framework that comprehensively solves the inverse wave scattering problem across all length scales. Our framework consists of the newly intro…
Wide-band butterfly network: stable and efficient inversion via multi-frequency neural networks
Matthew Li, Laurent Demanet, Leonardo Zepeda-Núñez
We introduce an end-to-end deep learning architecture called the wide-band butterfly network (WideBNet) for approximating the inverse scattering map from wide-band scattering data.…