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

Abstract

The paper reviews and extends an emerging body of theoretical results on deep learning including the conditions under which it can be exponentially better than shallow learning. A class of deep convolutional networks represent an important special case of these conditions, though weight sharing is not the main reason for their exponential advantage. Implications of a few key theorems are discussed, together with new results, open problems and conjectures.

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Document Details

Document Type
Technical Report
Publication Date
Mar 14, 2017
Accession Number
AD1070438

Entities

People

  • Brando Miranda
  • Hrushikesh Mhaskar
  • Lorenzo Rosasco
  • Qianli Liao
  • Tomaso Poggio

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence Software
  • Computations
  • Computer Languages
  • Computer Science
  • Computers
  • Convolutional Neural Networks
  • Deep Learning
  • Differential Equations
  • Machine Learning
  • Neural Networks
  • Numbers
  • Real Numbers
  • Real Variables
  • Standards
  • Trees (Data Structures)
  • Two Dimensional

Readers

  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks