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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.