66 citations · 124 across the 22 of their papers we have counts for
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math.ST2022★ 4 cited
Analysis of the rate of convergence of an over-parametrized deep neural network estimate learned by gradient descent
Michael Kohler, Adam Krzyzak
Estimation of a regression function from independent and identically distributed random variables is considered. The error with integration with respect to the design measure…
math.ST2022★ 2 cited
On the universal consistency of an over-parametrized deep neural network estimate learned by gradient descent
Selina Drews, Michael Kohler
Estimation of a multivariate regression function from independent and identically distributed data is considered. An estimate is defined which fits a deep neural network consisting…
stat.ML2022
Analysis of convolutional neural network image classifiers in a rotationally symmetric model
Michael Kohler, Benjamin Walter
Convolutional neural network image classifiers are defined and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk is…