@article {2557,
	title = {Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review},
	journal = {International Journal of Automation and Computing},
	year = {2017},
	month = {03/2017},
	pages = {1-17},
	abstract = {<p>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.</p>
},
	keywords = {convolutional neural networks, deep and shallow networks, deep learning, function approximation, Machine Learning, Neural Networks},
	doi = {10.1007/s11633-017-1054-2},
	url = {http://link.springer.com/article/10.1007/s11633-017-1054-2?wt_mc=Internal.Event.1.SEM.ArticleAuthorOnlineFirst},
	author = {Tomaso Poggio and Hrushikesh Mhaskar and Lorenzo Rosasco and Brando Miranda and Qianli Liao}
}
