Layer-wise Geometric Approximation Rates for Deep Networks

arXiv:2604. 20219v2 Announce Type: replace-cross Abstract: Depth is widely viewed as a central contributor to the success of deep neural networks, whereas standard neural network approximation theory typically provides guarantees only for the final output and leaves the role of intermediate layers largely unclear.