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- W4225513979 abstract "In this paper, we study the generalization performance of global minima for implementing empirical risk minimization (ERM) on over-parameterized deep ReLU nets. Using a novel deepening scheme for deep ReLU nets, we rigorously prove that there exist perfect global minima achieving almost optimal generalization error bounds for numerous types of data under mild conditions. Since over-parameterization is crucial to guarantee that the global minima of ERM on deep ReLU nets can be realized by the widely used stochastic gradient descent (SGD) algorithm, our results indeed fill a gap between optimization and generalization." @default.
- W4225513979 created "2022-05-05" @default.
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- W4225513979 date "2021-11-27" @default.
- W4225513979 modified "2023-09-23" @default.
- W4225513979 title "Generalization Performance of Empirical Risk Minimization on Over-parameterized Deep ReLU Nets" @default.
- W4225513979 hasPublicationYear "2021" @default.
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