Seok Hoan Choi

How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning

We show that deep neural networks (DNNs) can efficiently learn any composition of functions with bounded $F_{1}$-norm, which allows DNNs to break the curse of dimensionality in ways that shallow networks cannot. More specifically, we derive a generalization bound that combines a covering number argu…