期刊
INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
卷 14, 期 5, 页码 503-519出版社
SPRINGERNATURE
DOI: 10.1007/s11633-017-1054-2
关键词
Machine learning; neural networks; deep and shallow networks; convolutional neural networks; function approximation; deep learning
资金
- Center for Brains, Minds and Machines (CBMM)
- NSF STC award CCF [1231216]
- ARO [W911NF-15-1-0385]
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 not the main reason for their exponential advantage. Implications of a few key theorems are discussed, together with new results, open problems and conjectures.
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