no code implementations • NeurIPS 2023 • David Lüdke, Marin Biloš, Oleksandr Shchur, Marten Lienen, Stephan Günnemann
Autoregressive neural networks within the temporal point process (TPP) framework have become the standard for modeling continuous-time event data.
1 code implementation • 29 May 2023 • Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann
On this dataset, we show that our generative model captures the distribution of turbulent flows caused by unseen objects and generates high-quality, realistic samples amenable for downstream applications without access to any initial state.
no code implementations • 4 Apr 2023 • Johanna Sommer, Leon Hetzel, David Lüdke, Fabian Theis, Stephan Günnemann
Machine learning for molecules holds great potential for efficiently exploring the vast chemical space and thus streamlining the drug discovery process by facilitating the design of new therapeutic molecules.
1 code implementation • 14 Sep 2022 • David Lüdke, Tamaz Amiranashvili, Felix Ambellan, Ivan Ezhov, Bjoern Menze, Stefan Zachow
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population.