Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes

5 Aug 2019Kenji KawaguchiJiaoyang Huang

In this paper, we theoretically prove that gradient descent can find a global minimum of non-convex optimization of all layers for nonlinear deep neural networks of sizes commonly encountered in practice. The theory developed in this paper only requires the practical degrees of over-parameterization unlike previous theories... (read more)

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