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Stochastic computation


Cortese, John Anthony (1995) Stochastic computation. Dissertation (Ph.D.), California Institute of Technology.


This thesis approaches computation from a communication theory perspective. Data is given to a computer, which is asked to arrive at a binary hypothesis decision. The computation task is viewed as a signal drawn from an ensemble, corrupted by noise, and passed to a receiver which is asked to make a binary signal detection decision. To illustrate the approach, learning in a neural network is studied. An algorithm based on statistical communication techniques is developed which allows the determination of the neural network size, architecture, and system parameters. The computation, as interpreted in the communication framework, is assigned an equivalent channel capacity which measures the effectiveness with which the computing system extracts information in the Shannon sense from the input data. Numerical simulations of a neural network recognizing handwritten digits are used to illustrate key points.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Electrical Engineering
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Electrical Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Goodman, Rodney M.
Thesis Committee:
  • Franklin, Joel N.
  • Simon, Marvin K.
  • Goodman, Rodney M. (chair)
  • Abu-Mostafa, Yaser S.
  • Psaltis, Demetri
Defense Date:15 May 1995
Additional Information:Earned 2nd PhD from Caltech in 2004
Record Number:CaltechETD:etd-02202004-150303
Persistent URL:
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:684
Deposited By: Imported from ETD-db
Deposited On:25 Feb 2004
Last Modified:03 Dec 2014 00:11

Thesis Files

PDF (Cortese_ja_1995.pdf) - Final Version
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