A priori generalization error for two-layer ReLU neural network through minimum norm solution

6 Dec 2019Zhi-Qin John XuJiwei ZhangYaoyu ZhangChengchao Zhao

We focus on estimating \emph{a priori} generalization error of two-layer ReLU neural networks (NNs) trained by mean squared error, which only depends on initial parameters and the target function, through the following research line. We first estimate \emph{a priori} generalization error of finite-width two-layer ReLU NN with constraint of minimal norm solution, which is proved by \cite{zhang2019type} to be an equivalent solution of a linearized (w.r.t... (read more)

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