| Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks M Lienen, S Günnemann International Conference on Learning Representations, 2022 | 36 | 2022 |
| Scalable optimal transport in high dimensions for graph distances, embedding alignment, and more J Gasteiger, M Lienen, S Günnemann International Conference on Machine Learning, 5616-5627, 2021 | 15 | 2021 |
| FLGR: Fixed length gists representation learning for RNN-HMM hybrid-based neuromorphic continuous gesture recognition G Chen, J Chen, M Lienen, J Conradt, F Röhrbein, AC Knoll Frontiers in Neuroscience, 73, 2019 | 13 | 2019 |
| From Zero to Turbulence: Generative Modeling for 3D Flow Simulation M Lienen, D Lüdke, J Hansen-Palmus, S Günnemann International Conference on Learning Representations, 2024 | 11* | 2024 |
| torchode: A parallel ODE solver for pytorch M Lienen, S Günnemann arXiv preprint arXiv:2210.12375, 2022 | 3 | 2022 |
| Add and thin: Diffusion for temporal point processes D Lüdke, M Biloš, O Shchur, M Lienen, S Günnemann Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Assessing robustness via score-based adversarial image generation M Kollovieh, L Gosch, Y Scholten, M Lienen, S Günnemann arXiv preprint arXiv:2310.04285, 2023 | 2 | 2023 |
| Rate-adaptive Link Quality Estimation for Coded Packet Networks M Leclaire, SM Günther, M Lienen, M Riemensberger, G Carle 2016 IEEE 41st Conference on Local Computer Networks (LCN), 732-740, 2016 | 2 | 2016 |
Stephan GünnemannProfessor of Computer Science, Technical University of MunichVerified email at in.tum.de
