Unified Universality Theorem for Deep and Shallow Neural Networks with Joint-Group-Equivariant Feature Maps

This paper presents a novel, constructive proof for the universal approximation theorem of neural networks with joint-group-equivariant feature maps, unifying the understanding of approximation capabilities for both shallow and deep networks.