Expressivity of Deep Neural Networks
Ingo Gühring, Mones Raslan, Gitta Kutyniok · Cambridge University Press eBooks · 2022
In this chapter, we give a comprehensive overview of the large variety of approximation results for neural networks. Approximation rates for classical function spaces as well as the benefits of deep neural networks over shallow ones for specifically structured function classes are discussed. While the main body of existing results is for general feedforward architectures, we also review approximation results for convolutional, residual and recurrent neural networks.