Associative memories are conventionally used to represent data with very simple structure: sets of pairs of vectors. This paper describes a method for representing more complex compositionalâ€¦ (More)

Distributed representations are attractive for a number of reasons. They offer the possibility of representing concepts in a continuous space, they degrade gracefully with noise, and they can beâ€¦ (More)

One of the widely acknowledged drawbacks of exible statistical models is that they are often extremely di cult to interpret. However, if exible models are constrained to be additive they are muchâ€¦ (More)

Over the last few years a number of schemes for encoding compositional structure in distributed representations have been proposed, e.g., Smolensky's tensor products, Pollack's RAAMs, Plate's HRRs,â€¦ (More)

A set of sigma-pi units randomly connected to two input vectors forms a type of hetero-associator related to convolution- and matrix-based associative memories. Associations are represented asâ€¦ (More)

Definition: Convolution-Based Memory Models are a mathematical model of neural storage of complex data structures using distributed representations. Data structures stored range from lists of pairsâ€¦ (More)

One of the basic problems with distributed representations is coming up with the codes for the particular objects to be represented. A distributed representation that is useful for solving aâ€¦ (More)