  The random feature model exhibits a kind of resonance behavior when the
number of parameters is close to the training sample size. This behavior is
characterized by the appearance of large generalization gap, and is due to the
occurrence of very small eigenvalues for the associated Gram matrix. In this
paper, we examine the dynamic behavior of the gradient descent algorithm in
this regime. We show, both theoretically and experimentally, that there is a
dynamic self-correction mechanism at work: The larger the eventual
generalization gap, the slower it develops, both because of the small
eigenvalues. This gives us ample time to stop the training process and obtain
solutions with good generalization property.
