| Krylov methods for inverse problems: Surveying classical, and introducing new, algorithmic approaches S Gazzola, M Sabaté Landman GAMM‐Mitteilungen 43 (4), e202000017, 2020 | 25 | 2020 |
| Flexible GMRES for total variation regularization S Gazzola, M Sabaté Landman BIT Numerical Mathematics 59, 721-746, 2019 | 22 | 2019 |
| Iteratively reweighted FGMRES and FLSQR for sparse reconstruction S Gazzola, JG Nagy, MS Landman SIAM Journal on Scientific Computing 43 (5), S47-S69, 2021 | 13 | 2021 |
| Regularization by inexact Krylov methods with applications to blind deblurring S Gazzola, MS Landman SIAM Journal on Matrix Analysis and Applications 42 (4), 1528-1552, 2021 | 3 | 2021 |
| Latent-space disentanglement with untrained generator networks for the isolation of different motion types in video data A Abdullah, M Holler, K Kunisch, MS Landman International Conference on Scale Space and Variational Methods in Computer …, 2023 | 1 | 2023 |
| Autonomous Exploration and Identification of High Performing Adsorbents using Active Learning G Donval, C Hand, J Hook, E Dupont, MS Landman, M Freitag, M Lennox, ... | 1 | 2021 |
| Optimal Sparse Energy Sampling for X-ray Spectro-Microscopy: Reducing the X-ray Dose and Experiment Time Using Model Order Reduction PD Quinn, M Sabaté Landman, T Davis, M Freitag, S Gazzola, S Dolgov Chemical & Biomedical Imaging, 2024 | | 2024 |
| H-CMRH: a novel inner product free hybrid Krylov method for large-scale inverse problems AN Brown, MS Landman, JG Nagy arXiv preprint arXiv:2401.06918, 2024 | | 2024 |
| Augmented Flexible Krylov Subspace methods with applications to Bayesian inverse problems MS Landman, J Jiang, J Zhang, W Ren arXiv preprint arXiv:2310.05285, 2023 | | 2023 |
| Augmented Flexible Krylov Subspace methods with applications to Bayesian inverse problems M Sabate Landman, J Jiang, J Zhang, W Ren arXiv e-prints, arXiv: 2310.05285, 2023 | | 2023 |
| On Krylov methods for large-scale CBCT reconstruction MS Landman, A Biguri, S Hatamikia, R Boardman, J Aston, CB Schönlieb Physics in Medicine & Biology 68 (15), 155008, 2023 | | 2023 |
| Flexible Krylov Methods for Group Sparsity Regularization J Chung, MS Landman arXiv preprint arXiv:2306.08499, 2023 | | 2023 |
| Latent-space disentanglement with untrained generator networks allows to isolate different motion types in video data A Abdullah, M Holler, K Kunisch, MS Landman | | 2022 |
| Nonlinear motion separation via untrained generator networks with disentangled latent space variables and applications to cardiac MRI. M Holler, K Kunisch, MS Landman arXiv preprint arXiv:2205.10367, 2022 | | 2022 |
| Iteratively Reweighted FGMRES and FLSQR S Gazzola, MS Landman, JG Nagy SIAM Journal on Scientific Computing, 2020 | | 2020 |
| Iteratively Reweighted FGMRES for Sparse Reconstruction S Gazzola, JG Nagy, MS Landman XXI Householder Symposium on Numerical Linear Algebra, 350, 2020 | | 2020 |
| Adaptive Regularization Parameter Choice Rules for Large-Scale Problems S Gazzola, MS Landman arXiv preprint arXiv:1907.05666, 2019 | | 2019 |
| Adaptive Sampling for Imaging MS Landman, S Dolgov, S Gazzola, T Davis | | 2019 |
| Inverse Problems (L16) MS Landman, S Mukherjee | | |
| Hybrid Iterative Solver for Inverse Problems A Brown, J Nagy, MS Landman 2024 Joint Mathematics Meetings (JMM 2024), 0 | | |
