Stable architectures for deep neural networks E Haber, L Ruthotto Inverse problems 34 (1), 014004, 2017 | 774 | 2017 |
Deep neural networks motivated by partial differential equations L Ruthotto, E Haber Journal of Mathematical Imaging and Vision 62 (3), 352-364, 2020 | 502 | 2020 |
Reversible architectures for arbitrarily deep residual neural networks B Chang, L Meng, E Haber, L Ruthotto, D Begert, E Holtham Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 298 | 2018 |
An introduction to deep generative modeling L Ruthotto, E Haber GAMM‐Mitteilungen 44 (2), e202100008, 2021 | 227 | 2021 |
hMRI–A toolbox for quantitative MRI in neuroscience and clinical research K Tabelow, E Balteau, J Ashburner, MF Callaghan, B Draganski, G Helms, ... Neuroimage 194, 191-210, 2019 | 227 | 2019 |
A machine learning framework for solving high-dimensional mean field game and mean field control problems L Ruthotto, SJ Osher, W Li, L Nurbekyan, SW Fung Proceedings of the National Academy of Sciences 117 (17), 9183-9193, 2020 | 212 | 2020 |
A hyperelastic regularization energy for image registration M Burger, J Modersitzki, L Ruthotto SIAM J. Sci. Comput. 35 (1), B132-B148, 2013 | 181 | 2013 |
Motion Correction in Dual Gated Cardiac PET using Mass-Preserving Image Registration F Gigengack, L Ruthotto, M Burger, C Wolters, X Jiang, K Schafers Medical Imaging, IEEE Transactions on 31 (3), 698-712, 2011 | 160 | 2011 |
Diffeomorphic Susceptibility Artefact Correction of Diffusion-Weighted Magnetic Resonance Images L Ruthotto, H Kugel, J Olesch, B Fischer, J Modersitzki, M Burger, ... Physics in Medicine and Biology 57, 5715-5731, 2012 | 144 | 2012 |
OT-flow: Fast and accurate continuous normalizing flows via optimal transport D Onken, SW Fung, X Li, L Ruthotto 35th AAAI Conference on Artificial Intelligence, 2020 | 133 | 2020 |
Layer-parallel training of deep residual neural networks S Gunther, L Ruthotto, JB Schroder, EC Cyr, NR Gauger SIAM Journal on Mathematics of Data Science 2 (1), 1-23, 2020 | 113 | 2020 |
Learning across scales---multiscale methods for convolution neural networks E Haber, L Ruthotto, E Holtham, SH Jun Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 108 | 2018 |
A multiscale finite volume method for Maxwell's equations at low frequencies E Haber, L Ruthotto Geophysical Journal International 199 (2), 1268-1277, 2014 | 64 | 2014 |
A neural network approach for high-dimensional optimal control applied to multiagent path finding D Onken, L Nurbekyan, X Li, SW Fung, S Osher, L Ruthotto IEEE Transactions on Control Systems Technology 31 (1), 235-251, 2022 | 48 | 2022 |
Discretize-optimize vs. optimize-discretize for time-series regression and continuous normalizing flows D Onken, L Ruthotto arXiv preprint arXiv:2005.13420, 2020 | 48 | 2020 |
IMEXnet a forward stable deep neural network E Haber, K Lensink, E Treister, L Ruthotto International Conference on Machine Learning, 2525-2534, 2019 | 48 | 2019 |
Hyperelastic susceptibility artifact correction of DTI in SPM L Ruthotto, S Mohammadi, C Heck, J Modersitzki, N Weiskopf Bildverarbeitung für die Medizin 2013: Algorithmen-Systeme-Anwendungen …, 2013 | 44 | 2013 |
jInv--a flexible Julia package for PDE parameter estimation L Ruthotto, E Treister, E Haber SIAM Journal on Scientific Computing 39 (5), S702-S722, 2017 | 43 | 2017 |
A Lagrangian Gauss--Newton--Krylov solver for mass-and intensity-preserving diffeomorphic image registration A Mang, L Ruthotto SIAM Journal on Scientific Computing 39 (5), B860-B885, 2017 | 42 | 2017 |
Example dataset for the hMRI toolbox MF Callaghan, A Lutti, J Ashburner, E Balteau, N Corbin, B Draganski, ... Data in brief 25, 104132, 2019 | 34 | 2019 |