@article {4185,
	title = {Theory I: Deep networks and the curse of dimensionality},
	journal = {Bulletin of the Polish Academy of Sciences: Technical Sciences},
	volume = {66},
	year = {2018},
	abstract = {<p>We review recent work characterizing the classes of functions for which deep learning can be exponentially better than shallow learning. Deep convolutional networks are a special case of these conditions, though weight sharing is not the main reason for their exponential advantage.</p>
},
	keywords = {convolutional neural networks, deep and shallow networks, deep learning, function approximation},
	author = {Tomaso Poggio and Qianli Liao}
}
