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Read More »Neural Stochastic Flows: Solver-Free Modelling and Inference for SDE Solutions
arXiv:2510.25769v1 Announce Type: cross Abstract: Stochastic differential equations (SDEs) are well suited to modelling noisy and irregularly sampled time series found in finance, physics, and machine learning. Traditional approaches require costly numerical solvers to sample between arbitrary time points. We introduce Neural Stochastic Flows (NSFs) and their latent variants, which directly learn (latent) SDE transition laws using conditional normalising flows …
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