On the rate of convergence of an over-parametrized deep neural network regression estimate learned by gradient descent
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:2P73JOEPrecord.jsonopen to challenge →
read the original abstract
Nonparametric regression with random design is considered. The $L_2$ error with integration with respect to the design measure is used as the error criterion. An over-parametrized deep neural network regression estimate with logistic activation function is defined, where all weights are learned by gradient descent. It is shown that the estimate achieves a nearly optimal rate of convergence in case that the regression function is $(p,C)$--smooth.
This paper has not been read by Pith yet.
Forward citations
Cited by 1 Pith paper
-
Generalization in Deep Neural Networks: Minimax Rates for Gradient Methods
The paper derives the first minimax-optimal excess population risk rates for gradient descent and stochastic gradient descent on over-parameterized DNNs by linking their dynamics to kernel methods under polynomial wid...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.