26 citations · 91 across the 16 of their papers we have counts for
21 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…
Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks
Babak Maboudi Afkham, Julianne Chung, Matthias Chung
In this work, we describe a new approach that uses variational encoder-decoder (VED) networks for efficient goal-oriented uncertainty quantification for inverse problems. Contrary…
Hybrid Projection Methods for Solution Decomposition in Large-scale Bayesian Inverse Problems
Julianne Chung, Jiahua Jiang, Scot M. Miller +1
We develop hybrid projection methods for computing solutions to large-scale inverse problems, where the solution represents a sum of different stochastic components. Such scenarios…
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…
Hybrid Projection Methods for Large-scale Inverse Problems with Mixed Gaussian Priors
Taewon Cho, Julianne Chung, Jiahua Jiang
When solving ill-posed inverse problems, a good choice of the prior is critical for the computation of a reasonable solution. A common approach is to include a Gaussian prior, whic…
Sampled Limited Memory Methods for Massive Linear Inverse Problems
Julianne Chung, Matthias Chung, J. Tanner Slagel +1
In many modern imaging applications the desire to reconstruct high resolution images, coupled with the abundance of data from acquisition using ultra-fast detectors, have led to ne…
Sampled Tikhonov Regularization for Large Linear Inverse Problems
J. Tanner Slagel, Julianne Chung, Matthias Chung +2
In this paper, we investigate iterative methods that are based on sampling of the data for computing Tikhonov-regularized solutions. We focus on very large inverse problems where a…
Uncertainty quantification in large Bayesian linear inverse problems using Krylov subspace methods
Arvind K. Saibaba, Julianne Chung, Katrina Petroske
For linear inverse problems with a large number of unknown parameters, uncertainty quantification remains a challenging task. In this work, we use Krylov subspace methods to approx…
Flexible Krylov methods for regularization
Julianne Chung, Silvia Gazzola
In this paper we develop flexible Krylov methods for efficiently computing regularized solutions to large-scale linear inverse problems with an fit-to-data term and an $\e…
Optimal Experimental Design for Constrained Inverse Problems
Lars Ruthotto, Julianne Chung, Matthias Chung
In this paper, we address the challenging problem of optimal experimental design (OED) of constrained inverse problems. We consider two OED formulations that allow reducing the exp…
Efficient generalized Golub-Kahan based methods for dynamic inverse problems
Julianne Chung, Arvind K. Saibaba, Matthew Brown +1
We consider efficient methods for computing solutions to and estimating uncertainties in dynamic inverse problems, where the parameters of interest may change during the measuremen…
Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems
Julianne Chung, Matthias Chung, J. Tanner Slagel +1
We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant c…
Motion Estimation and Correction in Photoacoustic Tomographic Reconstruction
Julianne Chung, Linh Nguyen
Motion, e.g., due to patient movement or improper device calibration, is inevitable in many imaging modalities such as photoacoustic tomography (PAT) by a rotating system and can l…
Generalized hybrid iterative methods for large-scale Bayesian inverse problems
Julianne Chung, Arvind K. Saibaba
We develop a generalized hybrid iterative approach for computing solutions to large-scale Bayesian inverse problems. We consider a hybrid algorithm based on the generalized Golub-K…
Optimal regularized inverse matrices for inverse problems
Julianne Chung, Matthias Chung
In this paper, we consider optimal low-rank regularized inverse matrix approximations and their applications to inverse problems. We give an explicit solution to a generalized rank…
Optimal Regularization Parameters for General-Form Tikhonov Regularization
Julianne Chung, Malena I. Español, Tuan Nguyen
In this work we consider the problem of finding optimal regularization parameters for general-form Tikhonov regularization using training data. We formulate the general-form Tikhon…
An Efficient Approach for Computing Optimal Low-Rank Regularized Inverse Matrices
Julianne Chung, Matthias Chung
Standard regularization methods that are used to compute solutions to ill-posed inverse problems require knowledge of the forward model. In many real-life applications, the forward…