Doosan Jung

Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions

Neural Stochastic Differential Equations (Neural SDEs) have emerged as powerful mesh-free generative models for continuous stochastic processes, with critical applications in fields such as finance, physics, and biology. Previous state-of-the-art methods have relied on adversarial training, such as…