Optimal Learning Rates of Deep Convolutional Neural Networks: Additive Ridge Functions. (arXiv:2202.12119v1 [cs.LG])

Convolutional neural networks have shown extraordinary abilities in many
applications, especially those related to the classification tasks. However,
for the regression problem, the abilities of convolutional structures have not
been fully understood, and further investigation is needed. In this paper, we
consider the mean squared error analysis for deep convolutional neural
networks. We show that, for additive ridge functions, convolutional neural
networks followed by one fully connected layer with ReLU activation functions
can reach optimal mini-max rates (up to a …

arxiv convolutional neural networks learning networks neural networks ridge