Why deep neural networks for function approximation
Liang, Shiyu
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https://hdl.handle.net/2142/99417
Description
Title
Why deep neural networks for function approximation
Author(s)
Liang, Shiyu
Issue Date
2017-12-12
Director of Research (if dissertation) or Advisor (if thesis)
Srikant, Rayadurgam
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2018-03-13T15:49:14Z
Keyword(s)
Neural networks
Deep learning
Abstract
Recently there has been much interest in understanding why deep neural networks are preferred to shallow networks. We show that, for a large class of piecewise smooth functions, the number of neurons needed by a shallow network to approximate a function is exponentially larger than the corresponding number of neurons needed by a deep network for a given degree of function approximation. First, we consider univariate functions on a bounded interval and require a neural network to achieve an approximation error of ε uniformly over the interval. We show that shallow networks (i.e., networks whose depth does not depend on ε) require Ω(poly(1/ε)) neurons while deep networks (i.e., networks whose depth grows with 1/ε) require O(polylog(1/ε)) neurons. We then extend these results to certain classes of important multivariate functions. Our results are derived for neural networks which use a combination of rectifier linear units (ReLUs) and binary step units, two of the most popular types of activation functions. Our analysis builds on a simple observation: the multiplication of two bits can be represented by a ReLU.
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