Julianne Chung

Malena Sabaté Landman

CoRR, 2023

Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks.

[BibT_eX]

[DOI]

Babak Maboudi Afkham

CoRR, 2023

2022

slimTrain - A Stochastic Approximation Method for Training Separable Deep Neural Networks.

[BibT_eX]

[DOI]

SIAM J. Sci. Comput., August, 2022

2021

Hybrid Projection Methods with Recycling for Inverse Problems.

[BibT_eX]

[DOI]

Jiahua Jiang

Eric de Sturler

SIAM J. Sci. Comput., 2021

Research in Inverse Problems and Training in Computational Science: A Reflection on the Importance of Community.

[BibT_eX]

[DOI]

Comput. Sci. Eng., 2021

Efficient learning methods for large-scale optimal inversion design.

[BibT_eX]

[DOI]

CoRR, 2021

Computational methods for large-scale inverse problems: a survey on hybrid projection methods.

[BibT_eX]

[DOI]

Silvia Gazzola

CoRR, 2021

Learning Regularization Parameters of Inverse Problems via Deep Neural Networks.

[BibT_eX]

[DOI]

Babak Maboudi Afkham

CoRR, 2021

2020

Efficient Krylov subspace methods for uncertainty quantification in large Bayesian linear inverse problems.

[BibT_eX]

[DOI]

Arvind K. Saibaba

Katrina Petroske

Numer. Linear Algebra Appl., 2020

Hybrid Projection Methods for Large-scale Inverse Problems with Mixed Gaussian Priors.

[BibT_eX]

[DOI]

Taewon Cho

Jiahua Jiang

CoRR, 2020

2019

Flexible Krylov Methods for ℓ<sub>p</sub> Regularization.

[BibT_eX]

[DOI]

Silvia Gazzola

SIAM J. Sci. Comput., 2019

Sampled Limited Memory Methods for Massive Linear Inverse Problems.

[BibT_eX]

[DOI]

CoRR, 2019

Iterative Sampled Methods for Massive and Separable Nonlinear Inverse Problems.

[BibT_eX]

[DOI]

J. Tanner Slagel

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2019

2018

Optimal Experimental Design for Inverse Problems with State Constraints.

[BibT_eX]

[DOI]

Lars Ruthotto

SIAM J. Sci. Comput., 2018

2017

Generalized Hybrid Iterative Methods for Large-Scale Bayesian Inverse Problems.

[BibT_eX]

[DOI]

Arvind K. Saibaba

SIAM J. Sci. Comput., 2017

Optimal Regularized Inverse Matrices for Inverse Problems.

[BibT_eX]

[DOI]

SIAM J. Matrix Anal. Appl., 2017

Motion Estimation and Correction in Photoacoustic Tomographic Reconstruction.

[BibT_eX]

[DOI]

Linh Nguyen

SIAM J. Imaging Sci., 2017

Stochastic Newton and Quasi-Newton Methods for Large Linear Least-squares Problems.

[BibT_eX]

[DOI]

CoRR, 2017

2015

Large-Scale Inverse Problems in Imaging.

[BibT_eX]

[DOI]

Sarah Knepper

James G. Nagy

Proceedings of the Handbook of Mathematical Methods in Imaging, 2015

A Hybrid LSMR Algorithm for Large-Scale Tikhonov Regularization.

[BibT_eX]

[DOI]

Katrina Palmer

SIAM J. Sci. Comput., 2015

A Framework for Regularization via Operator Approximation.

[BibT_eX]

[DOI]

Julianne M. Chung

Misha E. Kilmer

SIAM J. Sci. Comput., 2015

2014

An Efficient Approach for Computing Optimal Low-Rank Regularized Inverse Matrices.

[BibT_eX]

[DOI]

CoRR, 2014

2013

Computing optimal low-rank matrix approximations for image processing.

[BibT_eX]

[DOI]

Proceedings of the 2013 Asilomar Conference on Signals, 2013

2012

Optimal Filters from Calibration Data for Image Deconvolution with Data Acquisition Error.

[BibT_eX]

[DOI]

J. Math. Imaging Vis., 2012

2011

Windowed Spectral Regularization of Inverse Problems.

[BibT_eX]

[DOI]

Glenn R. Easley

SIAM J. Sci. Comput., 2011

Designing Optimal Spectral Filters for Inverse Problems.

[BibT_eX]

[DOI]

SIAM J. Sci. Comput., 2011

2010

An Efficient Iterative Approach for Large-Scale Separable Nonlinear Inverse Problems.

[BibT_eX]

[DOI]

James G. Nagy

SIAM J. Sci. Comput., 2010

Numerical Algorithms for Polyenergetic Digital Breast Tomosynthesis Reconstruction.

[BibT_eX]

[DOI]

James G. Nagy

Ioannis Sechopoulos

SIAM J. Imaging Sci., 2010

High-Performance Three-Dimensional Image Reconstruction for Molecular Structure Determination.

[BibT_eX]

[DOI]