TRs available

[Paul Smolensky]

Three technical reports are available; please direct requests via e-mail to
  kate at boulder.colorado.edu 
or via regular mail to:
                           Paul Smolensky
                   Department of Computer Science
                       University of Colorado
                       Boulder, CO 80309-0430
Thanks -- paul

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                 Analyzing a connectionist model
                     as a system of soft rules

                 Clayton McMillan & Paul Smolensky

                  CU-CS-393-88       March, 1988

 In this paper we reexamine the knowledge  in  the  Rumelhart  and
 McClelland  (1986)  connectionist model of the acquisition of the
 English past tense.  We show that their original  connection  ma-
 trix  is  approximately  equivalent to one that can be explicitly
 decomposed into what we call soft rule matrices.  Each soft  rule
 matrix encodes the knowledge of how to handle the verbs in one of
 the verb classes determined for  this  task  by  Bybee  &  Slobin
 (1982).   This demonstrates one approximate but explicit sense in
 which it is reasonable to speak of the weights  in  connectionist
 networks  encoding  higher-level rules or schemas that operate in
 parallel.  Our results also suggest that it may  be  feasible  to
 understand the knowledge in connectionist networks at a level in-
 termediate between the microscopic level  of  individual  connec-
 tions and the monolithic level of the entire connection matrix.

                         To appear in the
 Proceedings of the Tenth Meeting of the Cognitive Science Society

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                     The constituent structure
                   of connectionist mental states:
                    A reply to Fodor and Pylyshyn

                           Paul Smolensky

                   CU-CS-394-88       March, 1988

  The primary purpose of this article is to reply  to  the  central
  point  of  Fodor and Pylyshyn's (1988) critique of connectionism.
  The direct reply to their critique comprises Section  2  of  this
  paper.   I  argue  that Fodor and Pylyshyn are simply mistaken in
  their claim that connectionist mental states lack  the  necessary
  constituent  structure,  and  that the basis of this mistake is a
  failure to appreciate the significance of distributed representa-
  tions  in  connectionist models.  Section 3 is a broader response
  to the bottom line of their critique, which is  that  connection-
  ists  should  re-orient  their work towards implementation of the
  classical symbolic cognitive architecture.  I argue instead  that
  connectionist  research  should develop new formalizations of the
  fundamental computational notions that have been given  one  par-
  ticular  formal  shape  in the traditional symbolic paradigm.  My
  response to Fodor and  Pylyshyn's  critique  presumes  a  certain
  meta-theoretical  context that is laid out in Section 1.  In this
  first section I argue that any discussion of the choice  of  some
  framework  for  cognitive modeling (e.g. the connectionist frame-
  work) must admit that such a choice embodies a response to a fun-
  damental cognitive paradox, and that this response shapes the en-
  tire  scientific  enterprise  surrounding  research  within  that
  framework.   Fodor  and  Pylyshyn  are  implicitly advocating one
  class of response to the paradox over another, their critique  is
  analyzed in this light.

       In the Southern Journal of Philosophy, special issue on
       Connectionism and the Foundations of Cognitive Science

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           Application of the Interactive Activation Model
                        to Document Retrieval

                    Jonathan Bein & Paul Smolensky

                       CU-CS-405-88   May 1988

  In this paper we consider an application of the  Interactive  Ac-
  tivation  Model  [McClelland  82]  to the problem of document re-
  trieval. The issues in this application center  around  a  neural
  net  or  "connectionist"  model  called inductive information Re-
  trieval set forth in [Mozer 84].  The  paper  provides  empirical
  results  on the robustness of this model using a real-world docu-
  ment database consisting of 13,000 documents.

                         To appear in the
  Proceedings of NeuroNimes: Neural Networks and their Applications