2 code implementations • 29 Oct 2023 • Damien Ferbach, Baptiste Goujaud, Gauthier Gidel, Aymeric Dieuleveut
The energy landscape of high-dimensional non-convex optimization problems is crucial to understanding the effectiveness of modern deep neural network architectures.
no code implementations • 9 Jun 2022 • Damien Ferbach, Christos Tsirigotis, Gauthier Gidel, Avishek, Bose
In this paper, we generalize the SLTH to functions that preserve the action of the group $G$ -- i. e. $G$-equivariant network -- and prove, with high probability, that one can approximate any $G$-equivariant network of fixed width and depth by pruning a randomly initialized overparametrized $G$-equivariant network to a $G$-equivariant subnetwork.