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Theoretical issues in deep networks
2020
Proceedings of the National Academy of Sciences of the United States of America
While deep learning is successful in a number of applications, it is not yet well understood theoretically. A theoretical characterization of deep learning should answer questions about their approximation power, the dynamics of optimization, and good out-of-sample performance, despite overparameterization and the absence of explicit regularization. We review our recent results toward this goal. In approximation theory both shallow and deep networks are known to approximate any continuous
doi:10.1073/pnas.1907369117
pmid:32518109
fatcat:ezfitsiwlze6tlezaapggq4fwi