The structure-mapping engine: algorithm and examples
Artificial Intelligence
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Analog retrieval by constraint satisfaction
Artificial Intelligence
BoltzCONS: dynamic symbol structures in a connectionist network
Artificial Intelligence - On connectionist symbol processing
Mapping part-whole hierarchies into connectionist networks
Artificial Intelligence - On connectionist symbol processing
Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Artificial Intelligence - On connectionist symbol processing
The handbook of brain theory and neural networks
Sparse Distributed Memory
Binary Spatter-Coding of Ordered K-Tuples
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Holographic reduced representations
IEEE Transactions on Neural Networks
A Binding Procedure for Distributed Binary Data Representations
Cybernetics and Systems Analysis
Knowledge representation with SOUL
Expert Systems with Applications: An International Journal
Localist approach to natural language processing
TELE-INFO'06 Proceedings of the 5th WSEAS international conference on Telecommunications and informatics
Similarity-Based Retrieval With Structure-Sensitive Sparse Binary Distributed Representations
Computational Intelligence
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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 framework of the associative-projective neural network architecture and the 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 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.