Training dynamics of Gaussian energy-based models decouple across eigenmodes of the empirical covariance, yielding exact early-stopping times, random-matrix finite-sample corrections, and a GCV-type train-test relation for energy-based models.
Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation
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A theoretical framework for overfitting in energy-based modeling
Training dynamics of Gaussian energy-based models decouple across eigenmodes of the empirical covariance, yielding exact early-stopping times, random-matrix finite-sample corrections, and a GCV-type train-test relation for energy-based models.