A Learning Algorithm for Boltzmann Machines.pdf


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COGNITIVE SCIENCE 9, 147-169 (1985)
A Learning Algorithm for
Boltzmann Machines*
DAVID H. ACKLEY
GEOFFREY E. HINTON
Computer Science Department
Carnegie-Mellon University
TERRENCE J. SEJNOWSKI
Biophysics Department
The Johns Hopkins University
The computotionol power of massively parallel networks of simple processing
elements resides in the communication bandwidth provided by the hardware
connections between elements. These connections con allow a significant
fraction of the knowledge of the system to be applied to an instance of a prob-
lem in o very short time. One kind of computation for which massively porollel
networks appear to be well suited is large constraint satisfaction searches,
but to use the connections efficiently two conditions must be met: First, a
search technique that is suitable for parallel networks must be found. Second,
there must be some way of choosing internal representations which allow the
preexisting hardware connectio


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