Lars Ruthotto
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Stable architectures for deep neural networks
E Haber, L Ruthotto
Inverse problems 34 (1), 014004, 2017
Deep neural networks motivated by partial differential equations
L Ruthotto, E Haber
Journal of Mathematical Imaging and Vision 62 (3), 352-364, 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
An introduction to deep generative modeling
L Ruthotto, E Haber
GAMM‐Mitteilungen 44 (2), e202100008, 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
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
A hyperelastic regularization energy for image registration
M Burger, J Modersitzki, L Ruthotto
SIAM J. Sci. Comput. 35 (1), B132-B148, 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
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
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
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
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
A multiscale finite volume method for Maxwell's equations at low frequencies
E Haber, L Ruthotto
Geophysical Journal International 199 (2), 1268-1277, 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
Discretize-optimize vs. optimize-discretize for time-series regression and continuous normalizing flows
D Onken, L Ruthotto
arXiv preprint arXiv:2005.13420, 2020
IMEXnet a forward stable deep neural network
E Haber, K Lensink, E Treister, L Ruthotto
International Conference on Machine Learning, 2525-2534, 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
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
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
Example dataset for the hMRI toolbox
MF Callaghan, A Lutti, J Ashburner, E Balteau, N Corbin, B Draganski, ...
Data in brief 25, 104132, 2019
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