Thomas Möllenhoff

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Daniel CremersTechnical University of MunichVerificeret mail på tum.de
Michael MoellerProfessor for Computer Vision, University of Siegen, GermanyVerificeret mail på uni-siegen.de
Emanuel LaudePostdoctoral Researcher, KU Leuven, BelgiumVerificeret mail på esat.kuleuven.be
Bjoern HaefnerNVIDIAVerificeret mail på nvidia.com
Yvain QuéauCNRS researcher at GREYC, Caen, FranceVerificeret mail på ensicaen.fr
Mohammad Emtiyaz KhanCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoVerificeret mail på postman.riken.jp
Evgeny StrekalovskiyTechnical University MunichVerificeret mail på in.tum.de
Jan LellmannUniversity of LuebeckVerificeret mail på uni-luebeck.de
Zhenzhang YeTechnical University of MunichVerificeret mail på tum.de
Thomas FrerixTechnical University of MunichVerificeret mail på tum.de
Tao WuHuawei TechnologiesVerificeret mail på huawei.com
Nico DaheimTU Darmstadt, ETH Zürich, ELLISVerificeret mail på ukp.informatik.tu-darmstadt.de
Iryna GurevychFull Professor, TU Darmstadt; Adjunct Professor, MBZUAI, Abu DhabiVerificeret mail på cs.tu-darmstadt.de
Claudia NieuwenhuisICSI, UC Berkeley, USAVerificeret mail på eecs.berkeley.edu
Edoardo M. PontiUniversity of Edinburgh / University of CambridgeVerificeret mail på ed.ac.uk
Peter NicklResearch Assistant, Center for Advanced Intelligence Project (AIP), RIKEN, TokyoVerificeret mail på riken.jp
Eren Mehmet KıralKeio University (Mathematical Science Team)Verificeret mail på keio.jp
Hannah DrögeUniversity of BonnVerificeret mail på uni-bonn.de
Dharmesh TailorUniversity of AmsterdamVerificeret mail på uva.nl
Pierre BréchetMax Planck Institute for Mathematics in the SciencesVerificeret mail på mis.mpg.de

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Thomas Möllenhoff

Andre navneThomas Moellenhoff, Thomas Mollenhoff

Research Scientist, RIKEN AIP

Verificeret mail på postman.riken.jp - Startside

Deep Learning Artificial Intelligence Machine Learning Optimization


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
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	76	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	51	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	46	2018
Proximal backpropagation T Frerix, T Möllenhoff, M Moeller, D Cremers International Conference on Learning Representations (ICLR), 2017	43	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
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
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
Controlling neural networks via energy dissipation M Moeller, T Mollenhoff, D Cremers Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019	23	2019
SAM as an Optimal Relaxation of Bayes T Möllenhoff, ME Khan arXiv preprint arXiv:2210.01620, 2022	21	2022
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	8	2022
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
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
Model Merging by Uncertainty-Based Gradient Matching N Daheim, T Möllenhoff, EM Ponti, I Gurevych, ME Khan arXiv preprint arXiv:2310.12808, 2023	4	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
Flat Metric Minimization with Applications in Generative Modeling T Möllenhoff, D Cremers International Conference on Machine Learning (ICML), 4626--4635, 2019	3	2019
The Lie-Group Bayesian Learning Rule EM Kiral, T Moellenhoff, ME Khan International Conference on Artificial Intelligence and Statistics, 3331-3352, 2023	2	2023
Sublabel-accurate multilabeling meets product label spaces Z Ye, B Haefner, Y Quéau, T Möllenhoff, D Cremers DAGM German Conference on Pattern Recognition, 3-17, 2021	2	2021
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 36, 2024	1	2024
A Cutting-Plane Method for Sublabel-Accurate Relaxation of Problems with Product Label Spaces Z Ye, B Haefner, Y Quéau, T Möllenhoff, D Cremers International Journal of Computer Vision 131 (1), 346-362, 2023	1	2023

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