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

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 globally optimal solution for general smooth and convex loss functions... (read more)

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