Deep vs. shallow networks : An approximation theory perspective

Deep vs. shallow networks : An approximation theory perspective

The paper briefly reviews several recent results on hierarchical architectures for learning from examples, that may formally explain the conditions under which Deep Convolutional Neural Networks perform much better in function approximation problems than shallow, one-hidden layer architectures. The...

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Bibliographic Details
Main Authors: Mhaskar, Hrushikesh, Poggio, Tomaso
Format: Technical Report
Language:en_US
Published: Center for Brains, Minds and Machines (CBMM), arXiv 2016
Subjects:
hierarchical architectures
Deep Convolutional Neural Networks
ReLU function
Gaussian networks
Online Access:http://hdl.handle.net/1721.1/103911

Internet

http://hdl.handle.net/1721.1/103911

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