Geometric Layer-wise Approximation Rates for Deep Networks

ArXi:2604.20219v1 Announce Type: new 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. We address this gap by developing a quantitative framework in which depth admits a precise scale-dependent interpretation.