arXiv stat.ML
· Papers
Deep Neural Variation Spaces: A Unifying Perspective on Depth and Complexity
arXiv:2607.05546v1 Announce Type: new Abstract: We develop a unified function space theory of deep fully connected neural networks. Functions in our spaces are defined recursively as $ell^1$-bounded linear combinations of activated functions from preceding layers, with a dictionary of affine functions at the first lay