Computation and Learning in Neural Networks With Binary Weights

Abstract

Under the aegis of the AFOSR grant they have been investigating computational learning attributes of networks of formal neurons. The formal neurons considered are linear threshold elements which produce binary outputs based on the sign of a linear form of a set of inputs. The researchers have been interested in (1) exploring the theoretical limitations on what can be computed or learnt in neural network architectures, and (2) developing and analyzing learning algorithms which specify weights as a function of a set of examples of a computation.

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Document Details

Document Type
Technical Report
Publication Date
Nov 28, 1992
Accession Number
ADA261182

Entities

People

  • Santosh S. Venkatesh

Organizations

  • Moore School of Electrical Engineering

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Brain
  • Computational Science
  • Computer Programming
  • Computers
  • Content Addressable Memory
  • Electrical Engineering
  • Information Processing
  • Information Systems
  • Information Theory
  • Machine Learning
  • Neural Networks
  • Random Variables
  • Recurrent Neural Networks
  • Simplex Method
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Computer Engineering
  • Operations Research

Technology Areas

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