66 citations · 124 across the 22 of their papers we have counts for
14 papers · 1 filter
Learning of deep neural network regression estimates using gradient descent with pruning
Michael Kohler, Vincent Molinero Römer, Adam Krzyżak
Estimation of a regression function from independent and identically distributed data is considered. The error with integration with respect to the design variable is used as…
Estimation of a regression function from dependent data by over-parametrized deep neural networks learned by gradient descent
Michael Kohler, Adam Krzyżak, Vincent Molinero Römer
Estimation of a regression function from exponentially -mixing data is considered. The error with integration with respect to the design is used as the error criterion. De…
Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization
Michael Kohler, Adam Krzyzak, Alisha Sänger
Image classification from independent and identically distributed random variables is considered. Image classifiers are defined which are based on a linear combination of deep conv…
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…
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…
On the rate of convergence of a classifier based on a Transformer encoder
Iryna Gurevych, Michael Kohler, Gözde Gül Sahin
Pattern recognition based on a high-dimensional predictor is considered. A classifier is defined which is based on a Transformer encoder. The rate of convergence of the misclassifi…