26 citations · 91 across the 16 of their papers we have counts for
4 papers · 1 filter
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…