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arXiv · 1611.00740

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

Tomaso Poggio·Hrushikesh Mhaskar·Lorenzo Rosasco·Brando Miranda·Qianli Liao
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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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Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao. 2017-02-04. Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review. https://arxiv.org/abs/1611.00740

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