| 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 | 67 | 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 | 49 | 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 | 47 | 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 | 29 | 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 |
| Controlling neural networks via energy dissipation M Moeller, T Mollenhoff, D Cremers Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 27 | 2019 |
| SAM as an Optimal Relaxation of Bayes T Möllenhoff, ME Khan arXiv preprint arXiv:2210.01620, 2022 | 23 | 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 | 21 | 2017 |
| Model Merging by Uncertainty-Based Gradient Matching N Daheim, T Möllenhoff, EM Ponti, I Gurevych, ME Khan arXiv preprint arXiv:2310.12808, 2023 | 17 | 2023 |
| 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 | 13 | 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 | 10 | 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 |
| 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 36, 2024 | 3 | 2024 |
| Conformal Prediction via Regression-as-Classification EK Guha, S Natarajan, T Möllenhoff, ME Khan, E Ndiaye The Twelfth International Conference on Learning Representations, 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 |
| 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 | 2 | 2024 |
| The Lie-Group Bayesian Learning Rule EM Kiral, T Moellenhoff, ME Khan International Conference on Artificial Intelligence and Statistics, 3331-3352, 2023 | 2 | 2023 |
Daniel CremersTechnical University of Munich在 tum.de 的电子邮件经过验证
