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This paper introduces concept modulation models (CMMs), a unified framework for identifiability and extrapolation in conditional generative models. It shows that feature agreement on observed attributes induces constraints through attribute potentials, enabling algebraic extrapolation criteria that recover and generalize existing results.
This paper develops a scaling limit theory for SGLD-Gibbs to provide principled hyperparameter tuning guidance for meaningful uncertainty quantification in large-scale latent variable models.