|Title||Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Poggio T., Mhaskar H, Rosasco L., Miranda B., Liao Q.|
|Journal||International Journal of Automation and Computing|
|Keywords||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.