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Found 4 papers, 2 papers with code
Add and Thin: Diffusion for Temporal Point Processes
Autoregressive neural networks within the temporal point process (TPP) framework have become the standard for modeling continuous-time event data.
From Zero to Turbulence: Generative Modeling for 3D Flow Simulation
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.
The power of motifs as inductive bias for learning molecular distributions
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.
Landmark-free Statistical Shape Modeling via Neural Flow Deformations
Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population.
