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arXiv · 2002.07867

Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology

Quynh Nguyen·Marco Mondelli
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Abstract

Recent works have shown that gradient descent can find a global minimum for over-parameterized neural networks where the widths of all the hidden layers scale polynomially with $N$ ($N$ being the number of training samples). In this paper, we prove that, for deep networks, a single layer of width $N$ following the input layer suffices to ensure a similar guarantee. In particular, all the remaining layers are allowed to have constant widths, and form a pyramidal topology. We show an application of our result to the widely used LeCun's initialization and obtain an over-parameterization requirement for the single wide layer of order $N^2.$

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Quynh Nguyen, Marco Mondelli. 2020-02-18. Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology. https://arxiv.org/abs/2002.07867

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