Abstract:We consider the idealized setting of gradient flow on the population risk for infinitely wide two-layer ReLU neural networks (without bias), and study the effect of symmetries on the learned parameters and predictors. We first describe a general class of symmetries which, when satisfied by the target function f * and the input distribution, are preserved by the dynamics. We then study more specific cases. When f * is odd, we show that the dynamics of the predictor reduces to that of a (non-linearly parameteriz… Show more
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