Center for Brains, Minds and Machines (CBMM), arXiv
Citation
arXiv:1611.00740v5
Series/Report no.
CBMM Memo Series;058
Abstract
[formerly titled "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 not the main reason for their exponential advantage. Implications of a few key theorems are discussed, together with new results, open problems and conjectures.
Subjects
Deep Learning
deep convolutional networks
Terms of Use
Attribution-NonCommercial-ShareAlike 3.0 United States
