4 papers
Latent Space Inference via Paired Autoencoders
Emma Hart, Bas Peters, Julianne Chung +1
This work describes a novel data-driven latent space inference framework built on paired autoencoders to handle observational inconsistencies when solving inverse problems. Our app…
Variable Projected Augmented Lagrangian Methods for Generalized Lasso Problems
Stefano Aleotti, Davide Bianchi, Florian Bossmann +2
We introduce variable projected augmented Lagrangian (VPAL) methods for solving generalized nonlinear Lasso problems with improved speed and accuracy. By eliminating the nonsmooth…
Good Things Come in Pairs: Paired Autoencoders for Inverse Problems
Matthias Chung, Bas Peters, Michael Solomon
In this book chapter, we discuss recent advances in data-driven approaches for inverse problems. In particular, we focus on the \emph{paired autoencoder} framework, which has prove…
Optimal Linear Baseline Models for Scientific Machine Learning
Alexander DeLise, Kyle Loh, Krish Patel +3
Across scientific domains, a fundamental challenge is to characterize and compute the mappings from underlying physical processes to observed signals and measurements. While nonlin…