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arXiv · 1708.01422

Exploring the Function Space of Deep-Learning Machines

Bo Li·David Saad
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Abstract

The function space of deep-learning machines is investigated by studying growth in the entropy of functions of a given error with respect to a reference function, realized by a deep-learning machine. Using physics-inspired methods we study both sparsely and densely-connected architectures to discover a layer-wise convergence of candidate functions, marked by a corresponding reduction in entropy when approaching the reference function, gain insight into the importance of having a large number of layers, and observe phase transitions as the error increases.

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Bo Li, David Saad. 2017-08-04. Exploring the Function Space of Deep-Learning Machines. https://doi.org/10.1103/physrevlett.120.248301

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