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We propose conditional flows of the maximum mean discrepancy (MMD) with the negative distance kernel for posterior sampling and conditional generative modeling.
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R. Jordan, D. Kinderlehrer, and F. Otto · 1998
Earlier work this paper cites.
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Earlier work this paper cites.
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