Paper Info
Title
Analysis of Distributed Representation of Constituent Structure in Connectionist Systems
Abstract
A general method, the tensor product representation, is described for the distributed representation of value/variable bindings. The method allows the fully distributed representation of symbolic structures: the roles in the structures, as well as the fillers for those roles, can be arbitrarily non-local. Fully and partially localized special cases reduce to existing cases of connectionist representations of structured data; the tensor product representation generalizes these and the few existing examples of fuUy distributed representations of structures. The representation saturates gracefully as larger structures are represented; it penn its recursive construction of complex representations from simpler ones; it respects the independence of the capacities to generate and maintain multiple bindings in parallel; it extends naturally to continuous structures and continuous representational patterns; it pennits values to also serve as variables; it enables analysis of the interference of symbolic structures stored in associative memories; and it leads to characterization of optimal distributed representations of roles and a recirculation algorithm for learning them.
Year
Venue
Field
1987
NIPS
Tensor product,Real representation,Associative property,Computer science,Theoretical computer science,Interference (wave propagation),Artificial intelligence,Data model,Distributed representation,Machine learning,Connectionism,Recursion
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
2
1
Name
Order
Citations
PageRank
Paul Smolensky121593.76