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math.NA2025
A Jump-Diffusion Framework for Irregular Time Series Generation
O. Pfohl, J. Chemseddine, P. Hagemann +3
We propose a framework for generative modeling of continuous-time processes from irregularly and asynchronously recorded data. It is based on the matching of generators and accommo…
cs.LG2025
Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans
Christian Wald, Gabriele Steidl
Among generative neural models, flow matching techniques stand out for their simple applicability and good scaling properties. Here, velocity fields of curves connecting a simple l…