8 citations · 9 across the 6 of their papers we have counts for
9 papers
Flexible Krylov Methods for Group Sparsity Regularization
Julianne Chung, Malena Sabaté Landman
This paper introduces new solvers for efficiently computing solutions to large-scale inverse problems with group sparsity regularization, including both non-overlapping and overlap…
Efficient learning methods for large-scale optimal inversion design
Julianne Chung, Matthias Chung, Silvia Gazzola +1
In this work, we investigate various approaches that use learning from training data to solve inverse problems, following a bi-level learning approach. We consider a general framew…
slimTrain -- A Stochastic Approximation Method for Training Separable Deep Neural Networks
Elizabeth Newman, Julianne Chung, Matthias Chung +1
Deep neural networks (DNNs) have shown their success as high-dimensional function approximators in many applications; however, training DNNs can be challenging in general. DNN trai…
Computational methods for large-scale inverse problems: a survey on hybrid projection methods
Julianne Chung, Silvia Gazzola
This paper surveys an important class of methods that combine iterative projection methods and variational regularization methods for large-scale inverse problems. Iterative method…
Learning Regularization Parameters of Inverse Problems via Deep Neural Networks
Babak Maboudi Afkham, Julianne Chung, Matthias Chung
In this work, we describe a new approach that uses deep neural networks (DNN) to obtain regularization parameters for solving inverse problems. We consider a supervised learning ap…
Hybrid Projection Methods with Recycling for Inverse Problems
Julianne Chung, Eric de Sturler, Jiahua Jiang
Iterative hybrid projection methods have proven to be very effective for solving large linear inverse problems due to their inherent regularizing properties as well as the added fl…