Expertises
Mathematics
# Activation Function
# Neural Networks
# Rate Of Convergence
# Regression Estimate
Business & Economics
# Activation
# Neural Networks
# Rate Of Convergence
Engineering & Materials Science
# Deep Neural Networks
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Publicaties
Recent
Kohler, M., Krzyzak, A.
, & Langer, S. (Accepted/In press).
Estimation of a function of low local dimensionality by deep neural networks.
IEEE transactions on information theory.
https://doi.org/10.1109/TIT.2022.3146620
Kohler, M., Krzyzak, A.
, & Langer, S. (2021).
Estimation of a function of low local dimensionality by deep neural networks. arXiv.org.
https://arxiv.org/abs/1908.11140
Kohler, M.
, Langer, S., & Reif, U. (2021).
Estimation of a regression function on a manifold by fully connected deep neural networks. arXiv.org.
Braun, A., Kohler, M.
, Langer, S., & Walk, H. (2021).
The smoking gun: Statistical theory improves neural network estimates. arXiv.org.
Langer, S. (2021).
Approximating smooth functions by deep neural networks with sigmoid activation function.
Journal of multivariate analysis,
182(C), [104696].
https://doi.org/10.1016/j.jmva.2020.104696
Langer, S. (2021).
Analysis of the rate of convergence of fully connected deep neural network regression estimates with smooth activation function.
Journal of multivariate analysis,
182(C), [104695].
https://doi.org/10.1016/j.jmva.2020.104695
Langer, S., & Kohler, M. (2021).
On the rate of convergence of fully connected deep neural network regression estimates.
Annals of statistics,
49(4), 2231-2249.
https://doi.org/10.1214/20-AOS2034
Kohler, M.
, & Langer, S. (2020).
Statistical theory for image classification using deep convolutional neural networks with cross entropy loss. arXiv.org.
Langer, S. (2020).
Ein Beitrag zur Statistischen Theorie des Deep Learnings. Verlag Dr. Hut.
Langer, S., & Kohler, M. (2020).
Discussion of: "Nonparametric regression using deep neural networks with ReLU activation function".
Annals of statistics,
48(4).
https://doi.org/10.1214/19-AOS1912
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