Landmark-Free Statistical Shape Modeling Via Neural Flow Deformations. - David Lüdke; Tamaz Amiranashvili; Felix Ambellan; Ivan Ezhov; Bjoern H. Menze; Stefan Zachow (MICCAI (2), 2022)

Learning Shape Reconstruction from Sparse Measurements with Neural Implicit Functions. - Tamaz Amiranashvili; David Lüdke; Hongwei Bran Li; Bjoern H. Menze; Stefan Zachow (MIDL, 2022)

Add and Thin: Diffusion for Temporal Point Processes. - David Lüdke; Marin Bilos; Oleksandr Shchur; Marten Lienen; Stephan Günnemann (NeurIPS, 2023)

Generative Diffusion for 3D Turbulent Flows. - Marten Lienen; Jan Hansen-Palmus; David Lüdke; Stephan Günnemann (CoRR, 2023)

The power of motifs as inductive bias for learning molecular distributions. - Johanna Sommer; Leon Hetzel; David Lüdke; Fabian J. Theis; Stephan Günnemann (CoRR, 2023)

From Zero to Turbulence: Generative Modeling for 3D Flow Simulation. - Marten Lienen; David Lüdke; Jan Hansen-Palmus; Stephan Günnemann (ICLR, 2024)

Learning continuous shape priors from sparse data with neural implicit functions. - Tamaz Amiranashvili; David Lüdke; Hongwei Bran Li; Stefan Zachow; Bjoern H. Menze (Medical Image Anal., 2024)

Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting. - Marcel Kollovieh; Marten Lienen; David Lüdke; Leo Schwinn; Stephan Günnemann (ICLR, 2025)

Unlocking Point Processes through Point Set Diffusion. - David Lüdke; Enric Rabasseda Raventós; Marcel Kollovieh; Stephan Günnemann (ICLR, 2025)

Joint Relational Database Generation via Graph-Conditional Diffusion Models. - Mohamed Amine Ketata; David Lüdke; Leo Schwinn; Stephan Günnemann (CoRR, 2025)