Oct 5, 2023 · We formalize an approach to this task within a generalization of the stochastic interpolant framework, leading to efficient learning algorithms.

The multimarginal perspective enables an efficient algorithm for reducing the dynamical transport cost in the ordinary two-marginal setting and formalizes ...

Generative models based on dynamical transport of measure, such as diffusion models, flow matching models, and stochastic interpolants, learn an ordinary or ...

The approach uses measure transport by randomized assignment flows on the statistical submanifold of factorizing distributions, which also enables to sample ...

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Oct 19, 2023 · Bibliographic details on Multimarginal generative modeling with stochastic interpolants.

Given a set of K probability densities, we consider the multimarginal generative modeling problem of learning a joint distribution that recovers these densities ...

Michael S. Albergo, N. M. Boffi, Michael Lindsey, and Eric Vanden-Eijnden. “Multimarginal generative modeling with stochastic interpolants.” International ...

Duration: 1:39:59
Posted: Oct 24, 2023