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ORCIDLars Ruthotto
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- affiliation: Emory University, Atlanta, GA, USA
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2020 – today
- 2024
[j22]Xingjian Li, Deepanshu Verma, Lars Ruthotto
:
A Neural Network Approach for Stochastic Optimal Control. SIAM J. Sci. Comput. 46(5): 535- (2024)- [i31]Lars Ruthotto:
Differential Equations for Continuous-Time Deep Learning. CoRR abs/2401.03965 (2024) - [i30]Abigail Julian, Lars Ruthotto:
PyHySCO: GPU-Enabled Susceptibility Artifact Distortion Correction in Seconds. CoRR abs/2403.10706 (2024) - 2023
- [j21]Moshe Eliasof, Jonathan Ephrath, Lars Ruthotto, Eran Treister:
MGIC: Multigrid-in-Channels Neural Network Architectures. SIAM J. Sci. Comput. 45(3): S307-S328 (2023) - [j20]Derek Onken, Levon Nurbekyan, Xingjian Li, Samy Wu Fung, Stanley J. Osher, Lars Ruthotto:
A Neural Network Approach for High-Dimensional Optimal Control Applied to Multiagent Path Finding. IEEE Trans. Control. Syst. Technol. 31(1): 235-251 (2023) - [c15]Moshe Eliasof, Lars Ruthotto, Eran Treister:
Improving Graph Neural Networks with Learnable Propagation Operators. ICML 2023: 9224-9245 - [i29]Paul Hagemann, Lars Ruthotto, Gabriele Steidl, Nicole Tianjiao Yang:
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generation. CoRR abs/2303.04772 (2023) - [i28]Alex Dunbar, Lars Ruthotto:
Alternating Minimization for Regression with Tropical Rational Functions. CoRR abs/2305.20072 (2023) - [i27]Zheyu Oliver Wang, Ricardo Baptista, Youssef M. Marzouk, Lars Ruthotto, Deepanshu Verma:
Efficient Neural Network Approaches for Conditional Optimal Transport with Applications in Bayesian Inference. CoRR abs/2310.16975 (2023) - [i26]Malvern Madondo, Deepanshu Verma, Lars Ruthotto, Nicholas Au Yong:
Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics. CoRR abs/2311.07563 (2023) - 2022
- [j19]Elizabeth Newman, Lars Ruthotto:
'hessQuik': Fast Hessian computation of composite functions. J. Open Source Softw. 7(72): 4171 (2022) - [j18]Elizabeth Newman, Julianne Chung, Matthias Chung, Lars Ruthotto:
slimTrain - A Stochastic Approximation Method for Training Separable Deep Neural Networks. SIAM J. Sci. Comput. 44(4): 2322- (2022) - [c14]Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus:
Multivariate Quantile Function Forecaster. AISTATS 2022: 10603-10621 - [i25]Kelvin Kan, François-Xavier Aubet, Tim Januschowski, Youngsuk Park, Konstantinos Benidis, Lars Ruthotto, Jan Gasthaus:
Multivariate Quantile Function Forecaster. CoRR abs/2202.11316 (2022) - [i24]Moshe Eliasof, Lars Ruthotto, Eran Treister:
ωGNNs: Deep Graph Neural Networks Enhanced by Multiple Propagation Operators. CoRR abs/2210.17224 (2022) - 2021
- [j17]Kelvin K. Kan, Samy Wu Fung, Lars Ruthotto:
PNKH-B: A Projected Newton-Krylov Method for Large-Scale Bound-Constrained Optimization. SIAM J. Sci. Comput. 43(5): S704-S726 (2021) - [j16]Elizabeth Newman, Lars Ruthotto, Joseph L. Hart, Bart G. van Bloemen Waanders:
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection. SIAM J. Math. Data Sci. 3(4): 1041-1066 (2021) - [c13]Derek Onken, Samy Wu Fung, Xingjian Li, Lars Ruthotto:
OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport. AAAI 2021: 9223-9232 - [c12]Derek Onken, Levon Nurbekyan, Xingjian Li, Samy Wu Fung, Stanley J. Osher, Lars Ruthotto:
A Neural Network Approach Applied to Multi-Agent Optimal Control. ECC 2021: 1036-1041 - [i23]Lars Ruthotto, Eldad Haber:
An Introduction to Deep Generative Modeling. CoRR abs/2103.05180 (2021) - [i22]Elizabeth Newman, Julianne Chung, Matthias Chung, Lars Ruthotto:
slimTrain - A Stochastic Approximation Method for Training Separable Deep Neural Networks. CoRR abs/2109.14002 (2021) - 2020
- [j15]Lars Ruthotto, Eldad Haber:
Deep Neural Networks Motivated by Partial Differential Equations. J. Math. Imaging Vis. 62(3): 352-364 (2020) - [j14]Jonathan Ephrath, Moshe Eliasof, Lars Ruthotto, Eldad Haber, Eran Treister:
LeanConvNets: Low-Cost Yet Effective Convolutional Neural Networks. IEEE J. Sel. Top. Signal Process. 14(4): 894-904 (2020) - [j13]Stefanie Günther, Lars Ruthotto, Jacob B. Schroder, Eric C. Cyr, Nicolas R. Gauger:
Layer-Parallel Training of Deep Residual Neural Networks. SIAM J. Math. Data Sci. 2(1): 1-23 (2020) - [j12]James L. Herring, James G. Nagy, Lars Ruthotto:
Gauss-Newton Optimization for Phase Recovery From the Bispectrum. IEEE Trans. Computational Imaging 6: 235-247 (2020) - [i21]Derek Onken, Lars Ruthotto:
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows. CoRR abs/2005.13420 (2020) - [i20]Kelvin K. Kan, Samy Wu Fung, Lars Ruthotto:
PNKH-B: A Projected Newton-Krylov Method for Large-Scale Bound-Constrained Optimization. CoRR abs/2005.13639 (2020) - [i19]Derek Onken, Samy Wu Fung, Xingjian Li, Lars Ruthotto:
OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport. CoRR abs/2006.00104 (2020) - [i18]Elizabeth Newman, Lars Ruthotto, Joseph L. Hart, Bart G. van Bloemen Waanders:
Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection. CoRR abs/2007.13171 (2020) - [i17]Moshe Eliasof, Jonathan Ephrath, Lars Ruthotto, Eran Treister:
Multigrid-in-Channels Neural Network Architectures. CoRR abs/2011.09128 (2020) - [i16]Kelvin K. Kan, James G. Nagy, Lars Ruthotto:
Avoiding The Double Descent Phenomenon of Random Feature Models Using Hybrid Regularization. CoRR abs/2012.06667 (2020)
2010 – 2019
- 2019
- [j11]Samy Wu Fung, Lars Ruthotto:
A multiscale method for model order reduction in PDE parameter estimation. J. Comput. Appl. Math. 350: 19-34 (2019) - [j10]Karsten Tabelow, Evelyne Balteau, John Ashburner, Martina F. Callaghan, Bogdan Draganski, Gunther Helms, Ferath Kherif, Tobias Leutritz, Antoine Lutti, Christophe Phillips, Enrico Reimer, Lars Ruthotto, Maryam Seif, Nikolaus Weiskopf, Gabriel Ziegler, Siawoosh Mohammadi:
hMRI - A toolbox for quantitative MRI in neuroscience and clinical research. NeuroImage 194: 191-210 (2019) - [j9]Samy Wu Fung, Lars Ruthotto:
An Uncertainty-Weighted Asynchronous ADMM Method for Parallel PDE Parameter Estimation. SIAM J. Sci. Comput. 41(5): S129-S148 (2019) - [c11]Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto:
IMEXnet A Forward Stable Deep Neural Network. ICML 2019: 2525-2534 - [i15]Samy Wu Fung, Sanna Tyrväinen, Lars Ruthotto, Eldad Haber:
Large-Scale Classification using Multinomial Regression and ADMM. CoRR abs/1901.09450 (2019) - [i14]Eldad Haber, Keegan Lensink, Eran Treister, Lars Ruthotto:
IMEXnet: A Forward Stable Deep Neural Network. CoRR abs/1903.02639 (2019) - [i13]Jonathan Ephrath, Lars Ruthotto, Eldad Haber, Eran Treister:
LeanResNet: A Low-cost yet Effective Convolutional Residual Networks. CoRR abs/1904.06952 (2019) - [i12]Jonathan Ephrath, Moshe Eliasof, Lars Ruthotto, Eldad Haber, Eran Treister:
LeanConvNets: Low-cost Yet Effective Convolutional Neural Networks. CoRR abs/1910.13157 (2019) - [i11]Lars Ruthotto, Stanley J. Osher, Wuchen Li, Levon Nurbekyan, Samy Wu Fung:
A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems. CoRR abs/1912.01825 (2019) - 2018
- [j8]Jan MacDonald, Lars Ruthotto:
Improved Susceptibility Artifact Correction of Echo-Planar MRI using the Alternating Direction Method of Multipliers. J. Math. Imaging Vis. 60(2): 268-282 (2018) - [j7]Lars Ruthotto, Julianne Chung, Matthias Chung:
Optimal Experimental Design for Inverse Problems with State Constraints. SIAM J. Sci. Comput. 40(4): B1080-B1100 (2018) - [c10]Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, Elliot Holtham:
Reversible Architectures for Arbitrarily Deep Residual Neural Networks. AAAI 2018: 2811-2818 - [c9]Eldad Haber, Lars Ruthotto, Elliot Holtham, Seong-Hwan Jun:
Learning Across Scales - Multiscale Methods for Convolution Neural Networks. AAAI 2018: 3142-3148 - [i10]Lars Ruthotto, Eldad Haber:
Deep Neural Networks motivated by Partial Differential Equations. CoRR abs/1804.04272 (2018) - [i9]Eran Treister, Lars Ruthotto, Michal Sharoni, Sapir Zafrani, Eldad Haber:
Low-Cost Parameterizations of Deep Convolution Neural Networks. CoRR abs/1805.07821 (2018) - [i8]Eldad Haber, Felix Lucka, Lars Ruthotto:
Never look back - A modified EnKF method and its application to the training of neural networks without back propagation. CoRR abs/1805.08034 (2018) - [i7]Stefanie Günther, Lars Ruthotto, Jacob B. Schroder, Eric C. Cyr, Nicolas R. Gauger:
Layer-Parallel Training of Deep Residual Neural Networks. CoRR abs/1812.04352 (2018) - 2017
- [j6]Lars Ruthotto, Chen Greif, Jan Modersitzki:
A stabilized multigrid solver for hyperelastic image registration. Numer. Linear Algebra Appl. 24(5) (2017) - [j5]Andreas Mang, Lars Ruthotto:
A Lagrangian Gauss-Newton-Krylov Solver for Mass- and Intensity-Preserving Diffeomorphic Image Registration. SIAM J. Sci. Comput. 39(5) (2017) - [j4]Lars Ruthotto, Eran Treister, Eldad Haber:
jInv-a Flexible Julia Package for PDE Parameter Estimation. SIAM J. Sci. Comput. 39(5) (2017) - [i6]Eldad Haber, Lars Ruthotto, Elliot Holtham:
Learning across scales - A multiscale method for Convolution Neural Networks. CoRR abs/1703.02009 (2017) - [i5]Andreas Mang, Lars Ruthotto:
A Lagrangian Gauss-Newton-Krylov Solver for Mass- and Intensity-Preserving Diffeomorphic Image Registration. CoRR abs/1703.04446 (2017) - [i4]Eldad Haber, Lars Ruthotto:
Stable Architectures for Deep Neural Networks. CoRR abs/1705.03341 (2017) - [i3]James L. Herring, James G. Nagy, Lars Ruthotto:
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled Variables. CoRR abs/1705.09992 (2017) - [i2]Bo Chang, Lili Meng, Eldad Haber, Lars Ruthotto, David Begert, Elliot Holtham:
Reversible Architectures for Arbitrarily Deep Residual Neural Networks. CoRR abs/1709.03698 (2017) - 2016
- [c8]Maximilian März, Lars Ruthotto:
Combined Background Field Removal and Reconstruction for Quantitative Susceptibility Mapping. Bildverarbeitung für die Medizin 2016: 8-13 - [i1]Lars Ruthotto, Eran Treister, Eldad Haber:
jInv - a flexible Julia package for PDE parameter estimation. CoRR abs/1606.07399 (2016) - 2015
- [r1]Lars Ruthotto, Jan Modersitzki:
Non-linear Image Registration. Handbook of Mathematical Methods in Imaging 2015: 2005-2051 - 2014
- [j3]Jennifer Fohring, Eldad Haber, Lars Ruthotto:
Geophysical Imaging of Fluid Flow in Porous Media. SIAM J. Sci. Comput. 36(5) (2014) - [c7]Constantin Heck, Lars Ruthotto, Jan Modersitzki, Benjamin Berkels:
Model-Based Parameterestimation in DCE-MRI Without an Arterial Input Function. Bildverarbeitung für die Medizin 2014: 246-251 - [c6]Lars Ruthotto, Siawoosh Mohammadi, Nikolaus Weiskopf:
A new method for joint susceptibility artefact correction and super-resolution for dMRI. Image Processing 2014: 90340P - 2013
- [j2]Martin Burger, Jan Modersitzki, Lars Ruthotto:
A Hyperelastic Regularization Energy for Image Registration. SIAM J. Sci. Comput. 35(1) (2013) - [c5]Lars Ruthotto, Siawoosh Mohammadi, Constantin Heck, Jan Modersitzki, Nikolaus Weiskopf:
Hyperelastic Susceptibility Artifact Correction of DTI in SPM. Bildverarbeitung für die Medizin 2013: 344-349 - 2012
- [b1]Lars Ruthotto:
Hyperelastic image registration: theory, numerical methods, and applications. University of Münster, 2012, pp. 1-113 - [j1]Fabian Gigengack, Lars Ruthotto, Martin Burger, Carsten H. Wolters, Xiaoyi Jiang, Klaus P. Schäfers:
Motion Correction in Dual Gated Cardiac PET Using Mass-Preserving Image Registration. IEEE Trans. Medical Imaging 31(3): 698-712 (2012) - [c4]Lars Ruthotto, Fabian Gigengack, Martin Burger, Carsten H. Wolters, Xiaoyi Jiang, Klaus P. Schäfers, Jan Modersitzki:
A Simplified Pipeline for Motion Correction in Dual Gated Cardiac PET. Bildverarbeitung für die Medizin 2012: 51-56 - [c3]Fabian Gigengack, Lars Ruthotto, Xiaoyi Jiang, Jan Modersitzki, Martin Burger, Sven Hermann, Klaus P. Schäfers:
Atlas-Based Whole-Body PET-CT Segmentation Using a Passive Contour Distance. MCV 2012: 82-92 - [c2]Lars Ruthotto, Erlend Hodneland, Jan Modersitzki:
Registration of Dynamic Contrast Enhanced MRI with Local Rigidity Constraint. WBIR 2012: 190-198 - 2010
- [c1]Janine Olesch, Lars Ruthotto, Harald Kugel, Stefan Skare, Bernd Fischer, Carsten H. Wolters:
A variational approach for the correction of field-inhomogeneities in EPI sequences. Image Processing 2010: 76230K
Coauthor Index
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