A single-objective rectified flow variant uses neural ODEs trained by regression to monotonically decrease a fixed convex transport cost while preserving marginal distributions.
Estimation of non-normalized statistical models by score matching
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Lecture notes unify stochastic calculus, generator matching, and finite-sample Wasserstein guarantees for continuous-time Markovian generative models.
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Rectified Flow: A Marginal Preserving Approach to Optimal Transport
A single-objective rectified flow variant uses neural ODEs trained by regression to monotonically decrease a fixed convex transport cost while preserving marginal distributions.
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Statistical Analysis of Markovian Generative Modeling
Lecture notes unify stochastic calculus, generator matching, and finite-sample Wasserstein guarantees for continuous-time Markovian generative models.