Nearly Minimal Over-Parametrization of Shallow Neural Networks
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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 (fully-connected) shallow networks, in the best case scenario, the existing theory requires quadratic over-parametrization as a function of the number of training samples. This paper establishes that linear overparametrization is sufficient to fit the training data, using a simple variant of the (stochastic) gradient descent. Crucially, unlike several related works, the training considered in this paper is not limited to the lazy regime in the sense cautioned against in [1, 2]. Beyond shallow networks, the framework developed in this work for over-parametrization is applicable to a variety of learning problems.
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