nips*92 preprint available

[Tony Plate]

Preprint available (to appear in C. L.  Giles, S. J.
Hanson, and J.  D. Cowan, editors, Advances in Neural
Information Processing Systems 5 (NIPS*92), Morgan Kaufmann,
San Mateo, CA)

          Holographic Recurrent Networks
 
                  Tony A. Plate
          Department of Computer Science
              University of Toronto
                Toronto, M5S 1A4
                     Canada
               tap at ai.utoronto.ca

                     ABSTRACT

Holographic Recurrent Networks (HRNs) are recurrent networks
which incorporate associative memory techniques for storing
sequential structure.  HRNs can be easily and quickly
trained using gradient descent techniques to generate
sequences of discrete outputs and trajectories through
continuous space.  The performance of HRNs is found to be
superior to that of ordinary recurrent networks on these
sequence generation tasks.

- Obtain by ftp from archive.cis.ohio-state.edu in pub/neuroprose.

- No hardcopy available.

- FTP procedure:

unix> ftp archive.cis.ohio-state.edu (or 128.146.8.52)
Name: anonymous
Password: neuron
ftp> cd pub/neuroprose
ftp> binary
ftp> get plate.nips5.ps.Z
ftp> quit
unix> uncompress plate.nips5.ps.Z
unix> lpr plate.nips5.ps (or however you print postscript)