On the Power of Over-parametrization in Neural Networks with Quadratic Activation.

ICML, (2018): 1328-1337

Cited: 162|Views39
EI

Abstract

We provide new theoretical insights on why over-parametrization is effective in learning neural networks. For a $k$ hidden node shallow network with quadratic activation and $n$ training data points, we show as long as $ k ge sqrt{2n}$, over-parametrization enables local search algorithms to find a emph{globally} optimal solution for gene...More

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