Author

A. Scheinker

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#machine learning Preprint Oct 2026

Gradient-Free Sampling from Generative Models via Stochastic Bounded Extremum Seeking

We introduce a sampling approach for energy- and score-based generative models that requires no gradient evaluations of the model. Replacing the drift term that would normally contain the score $\nabla_\mathbf{x} \log p_\theta(\bf{x})$ with a high-frequency dithered cosine of the model's \textit{value}, $\sqrt{\alpha\o...

A. Scheinker · 0 citations