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

Arthur Jacot, Seok Hoan Choi, Yuxiao Wen

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 way…