|Title||Why and When Can Deep - but Not Shallow - Networks Avoid the Curse of Dimensionality: a Review|
|Publication Type||CBMM Memo|
|Year of Publication||2016|
|Authors||Poggio T., Mhaskar H., Rosasco L., Miranda B., Liao Q.|
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.