Approximation of functions from Korobov spaces by deep convolutional neural networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number84
Journal / PublicationAdvances in Computational Mathematics
Issue number6
Online published7 Dec 2022
Publication statusPublished - Dec 2022



The efficiency of deep convolutional neural networks (DCNNs) has been demonstrated empirically in many practical applications. In this paper, we establish a theory for approximating functions from Korobov spaces by DCNNs. It verifies rigorously the efficiency of DCNNs in approximating functions of many variables with some variable structures and their abilities in overcoming the curse of dimensionality.

Research Area(s)

  • Curse of dimensionality, Deep convolutional neural networks, Korobov spaces, Machine learning

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