Linear generative models memorize at small data loads but converge continuously once samples scale linearly with dimension; this convergence is insensitive to sharp recovery of principal latent factors.
& Seoane, B.A theoretical framework for overfitting in energy-based modelinginICML 2025-Forty-Second International Conference on Machine Learning (2025) (cit
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Memorisation, convergence and generalisation in generative models
Linear generative models memorize at small data loads but converge continuously once samples scale linearly with dimension; this convergence is insensitive to sharp recovery of principal latent factors.