NIPS preprint available
Tony Plate
tap at cs.toronto.edu
Tue Jan 11 08:15:42 EST 1994
Preprint Available:
To appear in J. D. Cowan, G. Tesauro, and J. Alspector,
editors, {\it Advances in Neural Information Processing
Systems - 6 - (NIPS*93)}, Morgan Kaufmann, San Mateo, CA
Estimating analogical similarity by dot-products
of Holographic Reduced Representations.
Tony A. Plate
Department of Computer Science
University of Toronto
Toronto, M5S 1A4
Canada
tap at ai.utoronto.ca
ABSTRACT
Models of analog retrieval require a computationally cheap
method of estimating similarity between a probe and the
candidates in a large pool of memory items. The vector
dot-product operation would be ideal for this purpose if it
were possible to encode complex structures as vector
representations in such a way that the superficial
similarity of vector representations reflected underlying
structural similarity. This paper describes how such an
encoding is provided by Holographic Reduced Representations
(HRRs), which are a method for encoding nested relational
structures as fixed-width distributed representations. The
conditions under which structural similarity is reflected in
the dot-product rankings of HRRs are discussed.
[This paper is possibly relevant to the recent discussion
of the binding problem on this list. In HRRs, I use
convolution (which can be thought of as a compressed
conjunctive code) to bind roles and fillers, and build up
distributed representations of hierarchical predicate
structures. This representation preserves the natural
similarity structure of predicates and objects.]
- Obtain by ftp from archive.cis.ohio-state.edu in pub/neuroprose.
- No hardcopy available.
- Software to perform the simulations 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.nips93.ps.Z
ftp> quit
unix> uncompress plate.nips93.ps.Z
unix> lpr plate.nips93.ps (or however you print postscript)
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