Prati
David Lüdke
David Lüdke
Technical University Munich
Potvrđena adresa e-pošte na tum.de
Naslov
Citirano
Citirano
Godina
From zero to turbulence: Generative modeling for 3d flow simulation
M Lienen, D Lüdke, J Hansen-Palmus, S Günnemann
arXiv preprint arXiv:2306.01776, 2023
37*2023
Learning shape reconstruction from sparse measurements with neural implicit functions
T Amiranashvili, D Lüdke, HB Li, B Menze, S Zachow
International Conference on Medical Imaging with Deep Learning, 22-34, 2022
302022
Landmark-free statistical shape modeling via neural flow deformations
D Lüdke, T Amiranashvili, F Ambellan, I Ezhov, BH Menze, S Zachow
International Conference on Medical Image Computing and Computer-Assisted …, 2022
212022
A multi-task deep learning method for detection of meniscal tears in MRI data from the osteoarthritis initiative database
A Tack, A Shestakov, D Lüdke, S Zachow
Frontiers in Bioengineering and Biotechnology 9, 747217, 2021
212021
Add and Thin: Diffusion for Temporal Point Processes
D Lüdke, M Biloš, O Shchur, M Lienen, S Günnemann
Neural Information Processing Systems (NeurIPS), 2023
132023
Learning continuous shape priors from sparse data with neural implicit functions
T Amiranashvili, D Lüdke, HB Li, S Zachow, BH Menze
Medical Image Analysis 94, 103099, 2024
62024
The power of motifs as inductive bias for learning molecular distributions
J Sommer, L Hetzel, D Lüdke, F Theis, S Günnemann
arXiv preprint arXiv:2306.17246, 2023
22023
Unlocking Point Processes through Point Set Diffusion
D Lüdke, ER Raventós, M Kollovieh, S Günnemann
arXiv preprint arXiv:2410.22493, 2024
12024
Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting
M Kollovieh, M Lienen, D Lüdke, L Schwinn, S Günnemann
arXiv preprint arXiv:2410.03024, 2024
12024
Neural flow-based deformations for statistical shape modelling
D Lüdke
Universitätsbibliothek Johann Christian Senckenberg, 2022
12022
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