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Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks
In this paper, we study the problem of learning a shallow artificial neural network that best fits a training data set. We study this problem in the over-parameterized regime where the numbers of observations are fewer than the number of parameters in the model. We show that with the quadratic activ
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