TY  - UNPB
ID  - cogprints546
UR  - http://cogprints.org/546/
A1  - Rachkovskij, Dmitri A.
TI  - Representation and processing of structures with binary sparse distributed codes
Y1  - 1999/06//
N2  - 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. In this paper we provide a comparative description of sparse binary distributed representation developed in the frames of the Associative-Projective Neural Network architecture and more well-known Holographic Reduced Representations of Plate and Binary Spatter Codes of Kanerva. 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 similarity of analogical episodes by the overlap of their codevectors is used in modeling of analogical reasoning for retrieval of analogs from memory and for analogical mapping.
AV  - public
KW  - Sparse coding
KW  -  binary coding
KW  -  binding
KW  -  representation of structure
KW  -  hierarchical representation
KW  -  nested representation
KW  -  long-term memory
KW  -  analogy
KW  -  compositional distributed representations
KW  -  connectionist symbol processing
KW  -  analogical retrieval
KW  -  analogical mapping.
ER  -

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