David Lüdke

According to our database, David Lüdke authored at least 10 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Joint Relational Database Generation via Graph-Conditional Diffusion Models.

Mohamed Amine Ketata, David Lüdke, Leo Schwinn, Stephan Günnemann

CoRR, May, 2025

Unlocking Point Processes through Point Set Diffusion.

David Lüdke, Enric Rabasseda Raventós, Marcel Kollovieh, Stephan Günnemann

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting.

Marcel Kollovieh, Marten Lienen, David Lüdke, Leo Schwinn, Stephan Günnemann

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

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

From Zero to Turbulence: Generative Modeling for 3D Flow Simulation.

Marten Lienen, David Lüdke, Jan Hansen-Palmus, Stephan Günnemann

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

Generative Diffusion for 3D Turbulent Flows.

Marten Lienen, Jan Hansen-Palmus, David Lüdke, Stephan Günnemann

CoRR, 2023

Add and Thin: Diffusion for Temporal Point Processes.

David Lüdke, Marin Bilos, Oleksandr Shchur, Marten Lienen, Stephan Günnemann

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Learning Shape Reconstruction from Sparse Measurements with Neural Implicit Functions.

Tamaz Amiranashvili, David Lüdke, Hongwei Bran Li, Bjoern H. Menze, Stefan Zachow

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

Landmark-Free Statistical Shape Modeling Via Neural Flow Deformations.

David Lüdke, Tamaz Amiranashvili, Felix Ambellan, Ivan Ezhov, Bjoern H. Menze, Stefan Zachow

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022