|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.
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