18 citations · 37 across the 9 of their papers we have counts for
Showing 2020Show all
3 papers · 1 filter
math.ST2020★ 10 cited
Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss under the hierarchical max-pooling model
Michael Kohler, Sophie Langer
Convolutional neural networks (CNNs) trained with cross-entropy loss have proven to be extremely successful in classifying images. In recent years, much work has been done to also…
math.ST2020
Analysis of the rate of convergence of fully connected deep neural network regression estimates with smooth activation function
Sophie Langer
This article contributes to the current statistical theory of deep neural networks (DNNs). It was shown that DNNs are able to circumvent the so--called curse of dimensionality in c…
cs.LG2020
Approximating smooth functions by deep neural networks with sigmoid activation function
Sophie Langer
We study the power of deep neural networks (DNNs) with sigmoid activation function. Recently, it was shown that DNNs approximate any -dimensional, smooth function on a compact s…