AINeutralarXiv – CS AI · 18h ago6/10
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Generation Properties of Stochastic Interpolation under Finite Training Set
Researchers derive closed-form expressions for optimal velocity fields in stochastic interpolation generative models trained on finite datasets, demonstrating that deterministic processes exactly recover training samples while stochastic processes add Gaussian noise. The work formalizes underfitting and overfitting for generative models, showing that estimation errors produce convex combinations of training samples with mixed noise corruption.