A McKean-Vlasov FBSDE generative model learns stochastic path laws that match observed terminal and time-marginal distributions via soft energy constraints rather than hard interpolation.
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A consistency-regularized Euclidean-Wasserstein-2 gradient flow performs joint posterior sampling and prompt optimization in latent space for efficient low-NFE inverse problem solving with diffusion models.
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Learning Generative Dynamics with Soft Law Constraints: A McKean-Vlasov FBSDE Approach
A McKean-Vlasov FBSDE generative model learns stochastic path laws that match observed terminal and time-marginal distributions via soft energy constraints rather than hard interpolation.
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Consistency Regularised Gradient Flows for Inverse Problems
A consistency-regularized Euclidean-Wasserstein-2 gradient flow performs joint posterior sampling and prompt optimization in latent space for efficient low-NFE inverse problem solving with diffusion models.