Selina Drews

most citedOn the universal consistency of an over-parametrized deep neural network estimate learned by gradient descent

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

stat.ML2023

Analysis of the expected $L_{2}$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization

Selina Drews, Michael Kohler

Recent results show that estimates defined by over-parametrized deep neural networks learned by applying gradient descent to a regularized empirical $L_{2}$ risk are universally cons…

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…

Analysis of the expected L2​ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization

On the universal consistency of an over-parametrized deep neural network estimate learned by gradient descent

Analysis of the expected $L_{2}$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization