Look for example the article "Approximation Spaces of Deep Neural Networks" by Gribonval et al. It contains a nice review of the spaces of functions that are r

Look for example the article "Approximation Spaces of Deep Neural Networks" by Gribonval et al. It contains a nice review of the spaces of functions that are represented by neural networks. It's just about properties of neural networks and their computing power, depending on their depth, width, skip connections, and nonlinearity type. The weights are fixed constants, there's no training whatsoever.