The paper characterizes 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.

Why and When Can Deep – but Not Shallow – Networks Avoid the Curse of Dimensionality: a Review

Lorenzo Rosasco;
2017

Abstract

The paper characterizes 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/888631
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