Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. A class of deep convolutional networks represent an important special case of these conditions, though weight sharing is n...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Automation, Chinese Academy of Sciences
2017
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| Online Access: | http://hdl.handle.net/1721.1/107679 https://orcid.org/0000-0002-3944-0455 |