منابع مشابه
Holographic reduced representations
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 compositional structure in distributed representations. The method uses circular convolution to associate items, which are represented by vectors. Arbitrary variable bindings, short sequences of various lengths, simple f...
متن کاملHolographic Reduced Representations: Convolution Algebra for Compositional Distributed Representations
A solution to the problem of representing compositional structure using distributed representations is described. The method uses circular convolution to associate items, which are represented by vectors. Arbitrary variable bindings, short sequences of various lengths, frames, and reduced representations can be compressed into a xed width vector. These representations are items in their own rig...
متن کاملEstimating Analogical Similarity by Dot-Products of Holographic Reduced Representations
Models of analog retrieval require a computationally cheap method of estimating similarity between a probe and the candidates in a large pool of memory items. The vector dot-product operation would be ideal for this purpose if it were possible to encode complex structures as vector representations in such a way that the superficial similarity of vector representations reflected underlying struc...
متن کاملLearning the systematic transformation of holographic reduced representations
Holographic Reduced Representation is a representational scheme which allows for the representation of variable-sized structures in a distributed manner. It has been shown that these compositional structures can be transformed holistically. However, in order to do so, the transformation vector was constructed by hand. In this paper we present two methods of learning the holistic transformation ...
متن کاملEmbedding Probabilities in Predication Space with Hermitian Holographic Reduced Representations
Predication-based Semantic Indexing (PSI) is an approach to generating high-dimensional vector representations of concept-relation-concept triplets. In this paper, we develop a variant of PSI that accommodates estimation of the probability of encountering a particular predication (such as fluoxetine TREATS major depressive disorder) in a collection of predications concerning a concept of intere...
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ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks
سال: 1995
ISSN: 1045-9227
DOI: 10.1109/72.377968