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On the rate of convergence of an over-parametrized deep neural network regression estimate learned by gradient descent
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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.
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Cited by 1 Pith paper
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