Convex Geometry and Duality of Over-parameterized Neural Networks

25 Feb 2020 Tolga Ergen Mert Pilanci

We develop a convex analytic approach to analyze finite width two-layer ReLU networks. We first prove that an optimal solution to the regularized training problem can be characterized as extreme points of a convex set, where simple solutions are encouraged via its convex geometrical properties... (read more)

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