66 citations · 124 across the 22 of their papers we have counts for
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Analysis of the rate of convergence of neural network regression estimates which are easy to implement
Alina Braun, Michael Kohler, Adam Krzyzak
Recent results in nonparametric regression show that for deep learning, i.e., for neural network estimates with many hidden layers, we are able to achieve good rates of convergence…
On the rate of convergence of a neural network regression estimate learned by gradient descent
Alina Braun, Michael Kohler, Harro Walk
Nonparametric regression with random design is considered. Estimates are defined by minimzing a penalized empirical risk over a suitably chosen class of neural networks with…
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…
On the rate of convergence of fully connected very deep neural network regression estimates
Michael Kohler, Sophie Langer
Recent results in nonparametric regression show that deep learning, i.e., neural network estimates with many hidden layers, are able to circumvent the so-called curse of dimensiona…