most citedDistribution learning via neural differential equations: a nonparametric statistical perspective

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cs.LG2025

Distribution learning via neural differential equations: minimal energy regularization and approximation theory

Youssef Marzouk, Zhi Ren, Jakob Zech

Neural ordinary differential equations (ODEs) provide expressive representations of invertible transport maps that can be used to approximate complex probability distributions, e.g…