|Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
|Year of Publication
|Poggio T., Mhaskar H, Rosasco L., Miranda B., Liao Q.
|International Journal of Automation and Computing
|convolutional neural networks, deep and shallow networks, deep learning, function approximation, Machine Learning, Neural Networks
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.