# Over-parameterization as a Catalyst for Better Generalization of Deep ReLU network

@article{Tian2019OverparameterizationAA, title={Over-parameterization as a Catalyst for Better Generalization of Deep ReLU network}, author={Yuandong Tian}, journal={ArXiv}, year={2019}, volume={abs/1909.13458} }

To analyze deep ReLU network, we adopt a student-teacher setting in which an over-parameterized student network learns from the output of a fixed teacher network of the same depth, with Stochastic Gradient Descent (SGD). Our contributions are two-fold. First, we prove that when the gradient is zero (or bounded above by a small constant) at every data point in training, a situation called \emph{interpolation setting}, there exists many-to-one \emph{alignment} between student and teacher nodes in…

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