Path: utzoo!utgpu!news-server.csri.toronto.edu!cs.utexas.edu!usc!aero!abbott
From: abbott@aerospace.aero.org (Russell J. Abbott)
Newsgroups: comp.ai.neural-nets
Subject: Meta-nets
Message-ID: <68940@aerospace.AERO.ORG>
Date: 16 Mar 90 17:30:59 GMT
Reply-To: abbott@itro3.aero.org (Russell J. Abbott)
Organization: The Aerospace Corporation, El Segundo, CA
Lines: 34


Has there been any work on building neural nets that find weights for
neural nets?  For example, suppose one wanted to construct a neural-net
to recognize handwritten letters.  Traditionally one would use a
learning algorithm to construct a set of weights.  Why not instead build
a meta-net that was trained to take a set of category instances and
produce a set of weights that would differentiate among the given
categories?

Presumably such a meta-net would be bigger than the nets for which it
was finding weights (or a diagonalization could be constructed) and it
would probably be difficult to train it.  But is there any a priori
reason why such a meta-net could not be built?

Input to such a meta-net might be something like an array of instances
of the desired categories.  Each column would correspond to a category;
the entries in each column would be examples of that category.  The
output would be a set of weights for a nerual net of a given
architecture.

The meta-net could be trained in a number of ways.  One way would simply
be to compare the output weights to weights produced for those
categories by a traditionally trained net.  Another way would be by
actually applying the output weights to the given instances in an
application-level net to see how well they categorized the examples.

In any event, since neural net training is a highly parallel and
continuous process and since neural nets tend to be most applicable to
highly parallel and continuous tasks, production of neural net weights
would seem to be the sort of job for which neural nets are well suited.


-- 
-- Russ abbott@itro3.aero.org