9 citations · 14 across the 4 of their papers we have counts for
4 papers · 1 filter
Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent
Michael Kohler, Adam Krzyzak, Benjamin Walter
Image classification based on over-parametrized convolutional neural networks with a global average-pooling layer is considered. The weights of the network are learned by gradient…
On the rate of convergence of a deep recurrent neural network estimate in a regression problem with dependent data
Michael Kohler, Adam Krzyzak
A regression problem with dependent data is considered. Regularity assumptions on the dependency of the data are introduced, and it is shown that under suitable structural assumpti…
On the rate of convergence of image classifiers based on convolutional neural networks
M. Kohler, A. Krzyzak, B. Walter
Image classifiers based on convolutional neural networks are defined, and the rate of convergence of the misclassification risk of the estimates towards the optimal misclassificati…
Estimation of a function of low local dimensionality by deep neural networks
Michael Kohler, Adam Krzyzak, Sophie Langer
Deep neural networks (DNNs) achieve impressive results for complicated tasks like object detection on images and speech recognition. Motivated by this practical success, there is n…