Preprint available

[Sreerupa Das]

The following paper has been placed in the Neuroprose archive.
Instructions for retrieving and printing follow the abstract.  This is
a preprint of the paper to appear in Advances in Neural Information
Processing Systems 5, 1993.  Comments and questions are welcome.

Thanks to Jordan Pollack for maintaining the archive!

Sreerupa Das                                  
Department of Computer Science                
University of Colorado, Boulder CO-80309-0430    email: rupa at cs.colorado.edu




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             USING PRIOR KNOWLEDGE IN AN NNPDA TO LEARN 
                    CONTEXT-FREE LANGUAGES


Sreerupa Das              C. Lee Giles             Guo-Zheng Sun
Dept. of Comp. Sc.&       NEC Research Inst.       Inst. for Adv. Comp. Studies
Inst. of Cognitive Sc.    4 Independence Way       University of Maryland 
University of Colorado    Princeton, NJ 08540      College Park, MD 20742
Boulder, CO 80309-0430


                           ABSTRACT

Although considerable interest has been shown in language inference
and automata induction using recurrent neural networks, success of
these models has mostly been limited to regular languages.  We have
previously demonstrated that Neural Network Pushdown Automaton (NNPDA)
model is capable of learning deterministic context-free languages
(e.g., a^n b^n and parenthesis languages) from examples.  However,
the learning task is computationally intensive.  In this paper we
discuss some ways in which {\em a priori} knowledge about the task and
data could be used for efficient learning.  We also observe that such
knowledge is often an experimental prerequisite for learning
nontrivial languages (eg.  a^n b^n c b^m a^m).

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FTP INSTRUCTIONS


     unix% ftp archive.cis.ohio-state.edu (or 128.146.8.52)
     Name: anonymous
     Password: neuron
     ftp> cd pub/neuroprose
     ftp> binary
     ftp> get das.prior_knowledge.ps.Z
     ftp> bye
     unix% zcat das.prior_knowledge.ps.Z | lpr