Representation and processing of structures with binary sparse distributed codes

The schemes for compositional distributed representations include those allowing on-the-fly construction of fixed dimensionality codevectors to encode structures of various complexity. Similarity of such codevectors takes into account both structural and semantic similarity of represented structures...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 13; no. 2; pp. 261 - 276
Main Author: Rachkovskij, D.A.
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2001
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Arrays
Artificial intelligence
Cognition & reasoning
Computational modeling
Cybernetics
Distributed computing
Helium
Holography
Humans
Mathematical models
Neural networks
Neurons
Plates (structural members)
Representations
Semantics
Similarity
Tensile stress
Thinning
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Description
Summary:The schemes for compositional distributed representations include those allowing on-the-fly construction of fixed dimensionality codevectors to encode structures of various complexity. Similarity of such codevectors takes into account both structural and semantic similarity of represented structures. We provide a comparative description of sparse binary distributed representation developed in the framework of the associative-projective neural network architecture and the more well known holographic reduced representations of T.A. Plate (1995) and binary spatter codes of P. Kanerva (1996). The key procedure in associative-projective neural networks is context-dependent thinning which binds codevectors and maintains their sparseness. The codevectors are stored in structured memory array which can be realized as distributed auto-associative memory. Examples of distributed representation of structured data are given. Fast estimation of the similarity of analogical episodes by the overlap of their codevectors is used in the modeling of analogical reasoning both for retrieval of analogs from memory and for analogical mapping.
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ISSN:1041-4347
1558-2191
DOI:10.1109/69.917565