20 citations · 23 across the 7 of their papers we have counts for
3 papers · 1 filter
Subquadratic Overparameterization for Shallow Neural Networks
Chaehwan Song, Ali Ramezani-Kebrya, Thomas Pethick +2
Overparameterization refers to the important phenomenon where the width of a neural network is chosen such that learning algorithms can provably attain zero loss in nonconvex train…
Nearly Minimal Over-Parametrization of Shallow Neural Networks
Armin Eftekhari, ChaeHwan Song, Volkan Cevher
A recent line of work has shown that an overparametrized neural network can perfectly fit the training data, an otherwise often intractable nonconvex optimization problem. For (ful…
Fast and Provable ADMM for Learning with Generative Priors
Fabian Latorre Gómez, Armin Eftekhari, Volkan Cevher
In this work, we propose a (linearized) Alternating Direction Method-of-Multipliers (ADMM) algorithm for minimizing a convex function subject to a nonconvex constraint. We focus on…