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[A Buggy AI Program]

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Computer Sciences TR 793 (also in the proceedings of the 1988
connectionist models summer school, (ed) Sejnowski, Hinton, and
Touretzky, Morgan Kauffmann, San Mateo, CA) 

      A NETWORK OF NEURON-LIKE UNITS THAT LEARNS TO PERCEIVE
        BY GENERATION AS WELL AS REWEIGHTING OF ITS LINKS

                  Vasant Honavar and Leonard Uhr

                   Computer Sciences Department
                  University of Wisconsin-Madison
                    Madison, WI 53706.  U.S.A.

                             Abstract

     Learning in connectionist models typically involves the modif-
ication  of  weights  associated with the links between neuron-like
units; but the topology of the network does not change.  This paper
describes  a new connectionist learning mechanism for generation in
a network of neuron-like  elements  that  enables  the  network  to
modify  its  own  topology by growing links and recruiting units as
needed (possibly from a pool of available units). A combination  of
generation  and  reweighting  of  links, and appropriate brain-like
constraints on network topology, together with  regulatory  mechan-
isms and neuronal structures that monitor the network's performance
that enable the network to decide when to generate, is shown  capa-
ble  of discovering, through feedback-aided learning, substantially
more powerful, and potentially more practical, networks for percep-
tual recognition than those obtained through reweighting alone.

     The recognition cones  model  of  perception  (Uhr1972,  Hona-
var1987,  Uhr1987)  is  used  to demonstrate the feasibility of the
approach.  Results of simulations of carefully pre-designed  recog-
nition  cones  illustrate  the usefulness of brain-like topological
constraints such  as  near-neighbor  connectivity  and  converging-
diverging  heterarchies for the perception of complex objects (such
as houses) from digitized  TV  images.   In  addition,  preliminary
results  indicate  that  brain-structured recognition cone networks
can successfully  learn  to  recognize  simple  patterns  (such  as
letters of the alphabet, drawings of objects like cups and apples),
using generation-discovery as well as reweighting, whereas  systems
that attempt to learn using reweighting alone fail to learn.

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Computer Sciences TR 805

                Experimental Results Indicate that
    Generation, Local Receptive Fields and Global Convergence
       Improve Perceptual Learning in Connectionist Networks

                  Vasant Honavar and Leonard Uhr
                   Computer Sciences Department
                  University of Wisconsin-Madison


                             Abstract


     This paper presents and compares results for  three  types  of
connectionist networks:

[A]  Multi-layered converging networks of neuron-like  units,  with
     each unit connected to a small randomly chosen subset of units
     in the adjacent layers, that learn by  re-weighting  of  their
     links;

[B]  Networks of neuron-like  units  structured  into  successively
     larger  modules under brain-like topological constraints (such
     as layered, converging-diverging heterarchies and local recep-
     tive fields) that learn by re-weighting of their links;

[C]  Networks with brain-like structures that learn by  generation-
     discovery,  which  involves the growth of links and recruiting
     of units in addition to re-weighting of links.


     Preliminary empirical results from simulation  of  these  net-
works  for  perceptual recognition tasks show large improvements in
learning from using brain-like structures  (e.g.,  local  receptive
fields, global convergence) over networks that lack such structure;
further substantial improvements in learning result from the use of
generation  in addition to reweighting of links. We examine some of
the implications of these results for perceptual learning  in  con-
nectionist networks.


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