Approximation of functions from Korobov spaces by deep convolutional neural networks
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
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Detail(s)
| Original language | English |
|---|---|
| Article number | 84 |
| Journal / Publication | Advances in Computational Mathematics |
| Volume | 48 |
| Issue number | 6 |
| Online published | 7 Dec 2022 |
| Publication status | Published - Dec 2022 |
Link(s)
| DOI | DOI |
|---|---|
| Attachment(s) | Documents
Publisher's Copyright Statement
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| Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85143405579&origin=recordpage |
| Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(ffdae046-3ed9-40b7-8455-d56de8f4b74b).html |
Abstract
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
Citation Format(s)
Approximation of functions from Korobov spaces by deep convolutional neural networks. / Mao, Tong; Zhou, Ding-Xuan.
In: Advances in Computational Mathematics, Vol. 48, No. 6, 84, 12.2022.
In: Advances in Computational Mathematics, Vol. 48, No. 6, 84, 12.2022.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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