Thomas Moellenhoff

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Daniel CremersTechnical University of MunichVerified email at tum.de
Michael MoellerProfessor for Computer Vision, University of Siegen, GermanyVerified email at uni-siegen.de
Mohammad Emtiyaz KhanCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoVerified email at postman.riken.jp
Emanuel LaudePostdoctoral Researcher, KU Leuven, BelgiumVerified email at esat.kuleuven.be
Bjoern HaefnerNVIDIAVerified email at nvidia.com
Yvain QuéauCNRS researcher at GREYC, Caen, FranceVerified email at ensicaen.fr
Evgeny StrekalovskiyTechnical University MunichVerified email at in.tum.de
Jan LellmannUniversity of LuebeckVerified email at uni-luebeck.de
Nico DaheimTU Darmstadt, ETH Zürich, ELLISVerified email at ukp.informatik.tu-darmstadt.de
Zhenzhang YeTechnical University of MunichVerified email at tum.de
Iryna GurevychFull Professor, TU Darmstadt; Adjunct Professor, MBZUAI, UAE; Affiliated Professor, INSAIT, BulgariaVerified email at cs.tu-darmstadt.de
Thomas FrerixGoogle DeepMindVerified email at tum.de
Tao WuHuawei TechnologiesVerified email at huawei.com
Edoardo M. PontiUniversity of Edinburgh / NVIDIAVerified email at ed.ac.uk
Peter NicklPhD researcher, University of GenevaVerified email at unige.ch
Yuesong ShenTechnical University of MunichVerified email at tum.de
Bai CongTokyo Institute of TechnologyVerified email at m.titech.ac.jp
Rio YokotaProfessor, Institute of Science TokyoVerified email at rio.scrc.iir.isct.ac.jp
Etash K GuhaUniversity of Washington, SambaNova SystemsVerified email at cs.washington.edu
Eugene NdiayeMachine Learning Research, AppleVerified email at apple.com

Thomas Moellenhoff

Other namesThomas Möllenhoff, Thomas Mollenhoff

Research Scientist, RIKEN AIP

Verified email at postman.riken.jp - Homepage

Deep Learning Artificial Intelligence Machine Learning Optimization


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The primal-dual hybrid gradient method for semiconvex splittings T Möllenhoff, E Strekalovskiy, M Moeller, D Cremers SIAM Journal on Imaging Sciences 8 (2), 827-857, 2015	70	2015
Sublabel-accurate relaxation of nonconvex energies T Möllenhoff, E Laude, M Moeller, J Lellmann, D Cremers Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016	50	2016
Fight ill-posedness with ill-posedness: Single-shot variational depth super-resolution from shading B Haefner, Y Quéau, T Möllenhoff, D Cremers Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018	48	2018
Proximal backpropagation T Frerix, T Möllenhoff, M Moeller, D Cremers International Conference on Learning Representations (ICLR), 2017	44	2017
Sublabel-accurate convex relaxation of vectorial multilabel energies E Laude, T Möllenhoff, M Moeller, J Lellmann, D Cremers Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016	30	2016
Model Merging by Uncertainty-Based Gradient Matching N Daheim, T Möllenhoff, EM Ponti, I Gurevych, ME Khan arXiv preprint arXiv:2310.12808, 2023	28	2023
Controlling neural networks via energy dissipation M Moeller, T Mollenhoff, D Cremers Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019	28	2019
Low rank priors for color image regularization T Möllenhoff, E Strekalovskiy, M Möller, D Cremers Energy Minimization Methods in Computer Vision and Pattern Recognition: 10th …, 2015	28	2015
SAM as an Optimal Relaxation of Bayes T Möllenhoff, ME Khan arXiv preprint arXiv:2210.01620, 2022	26	2022
Sublabel-accurate discretization of nonconvex free-discontinuity problems T Möllenhoff, D Cremers Proceedings of the IEEE International Conference on Computer Vision, 1183-1191, 2017	24	2017
Lifting vectorial variational problems: a natural formulation based on geometric measure theory and discrete exterior calculus T Mollenhoff, D Cremers Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	15	2019
Lifting the convex conjugate in Lagrangian relaxations: a tractable approach for continuous Markov random fields H Bauermeister, E Laude, T Möllenhoff, M Moeller, D Cremers SIAM Journal on Imaging Sciences 15 (3), 1253-1281, 2022	11	2022
Conformal Prediction via Regression-as-Classification EK Guha, S Natarajan, T Möllenhoff, ME Khan, E Ndiaye The Twelfth International Conference on Learning Representations, 2023	8	2023
Variational Learning is Effective for Large Deep Networks Y Shen, N Daheim, B Cong, P Nickl, GM Marconi, C Bazan, R Yokota, ... arXiv preprint arXiv:2402.17641, 2024	6	2024
Combinatorial preconditioners for proximal algorithms on graphs T Möllenhoff, Z Ye, T Wu, D Cremers International Conference on Artificial Intelligence and Statistics, 38-47, 2018	6	2018
Flat Metric Minimization with Applications in Generative Modeling T Möllenhoff, D Cremers International Conference on Machine Learning (ICML), 4626--4635, 2019	5	2019
Efficient convex optimization for minimal partition problems with volume constraints T Möllenhoff, C Nieuwenhuis, E Töppe, D Cremers Energy Minimization Methods in Computer Vision and Pattern Recognition: 9th …, 2013	5	2013
The Memory-Perturbation Equation: Understanding Model's Sensitivity to Data P Nickl, L Xu, D Tailor, T Möllenhoff, MEE Khan Advances in Neural Information Processing Systems, 2023	4	2023
The Lie-Group Bayesian Learning Rule EM Kiral, T Moellenhoff, ME Khan International Conference on Artificial Intelligence and Statistics, 3331-3352, 2023	3	2023
Optimization of Graph Total Variation via Active-Set-based Combinatorial Reconditioning Z Ye, T Möllenhoff, T Wu, D Cremers International Conference on Artificial Intelligence and Statistics, 657-668, 2020	3	2020

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