Vector Representation for Sub-Graph Encoding to Resolve Entities

Guo, Jinhong K.; Brackle, David Van; Lofaso, Nicolas; Hofmann, Martin

doi:10.1016/j.procs.2016.09.342

Cited by 3 publications

(2 citation statements)

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“…4 presents examples of both directed and undirected graphs. Following earlier work on graph representations with hypervectors, e.g., in [Rachkovskij and Kussul, 2001], [Gayler and Levy, 2009], [Guo et al, 2016], we consider the following very simple mapping of graphs into hypervectors [Gayler and Levy, 2009]. A random hypervector is assigned to each vertex of the graph, according to Fig.…”

Section: ) N-gram Statisticsmentioning

confidence: 99%

Vector Symbolic Architectures as a Computing Framework for Nanoscale Hardware

Kleyko¹,

Davies²,

Frady³

et al. 2021

Preprint

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This article reviews recent progress in the development of the computing framework Vector Symbolic Architectures (also known as Hyperdimensional Computing). This framework is well suited for implementation in stochastic, nanoscale hardware and it naturally expresses the types of cognitive operations required for Artificial Intelligence (AI). We demonstrate in this article that the ring-like algebraic structure of Vector Symbolic Architectures offers simple but powerful operations on highdimensional vectors that can support all data structures and manipulations relevant in modern computing. In addition, we illustrate the distinguishing feature of Vector Symbolic Architectures, "computing in superposition," which sets it apart from conventional computing. This latter property opens the door to efficient solutions to the difficult combinatorial search problems inherent in AI applications. Vector Symbolic Architectures are Turing complete, as we show, and we see them acting as a framework for computing with distributed representations in myriad AI settings. This paper serves as a reference for computer architects by illustrating techniques and philosophy of VSAs for distributed computing and relevance to emerging computing hardware, such as neuromorphic computing.

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Section: ) N-gram Statisticsmentioning

confidence: 99%

Vector Symbolic Architectures as a Computing Framework for Nanoscale Hardware

Kleyko¹,

Davies²,

Frady³

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Note that such data representation schemes by similarity preserving binary vectors have been developed for objects represented by various data types, mainly for (feature) vectors (see survey in [131]), but also for structured data types such as sequences [102,72,85,86] and graphs [127,128,148,136,62,134]. A significant part of this research is developed in the framework of distributed representations [45,76,106,126,89], including binary sparse distributed representations [102,98,103,127,128,113,114,137,138,139,148,135,136,61,134,129,130,131,132,31,33] and dense distributed representations [75,76] (see [82,84,87,88,83] for examples of their applications).…”

Section: Generalization In Namsmentioning

confidence: 99%

Neural Autoassociative Memories for Binary Vectors: A Survey

Gritsenko¹,

Rachkovskij²,

Frolov³

et al. 2017

Kibern. vyčisl. teh.

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Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension.The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons).Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints.Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors. Introduction. Neural network models of autoassociative, distributed memory allow storage and retrieval of many items (vectors) where the number of stored items can exceed the vector dimension (the number of neurons in the network). This opens the possibility of a sublinear time search (in the number of stored items) for approximate nearest neighbors among vectors of high dimension.The purpose of this paper is to review models of autoassociative, distributed memory that can be naturally implemented by neural networks (mainly with local learning rules and iterative dynamics based on information locally available to neurons).Scope. The survey is focused mainly on the networks of Hopfield, Willshaw and Potts, that have connections between pairs of neurons and operate on sparse binary vectors. We (Online), ISSN 0454-9910 (Print). Киб. и выч. техн. 2017. № 2 (188) 6 discuss not only autoassociative memory, but also the generalization properties of these networks. We also consider neural networks with higher-order connections and networks with a bipartite graph structure for non-binary data with linear constraints.Conclusions. In conclusion we discuss the relations to similarity search, advantages and drawbacks of these techniques, and topics for further research. An interesting and still not completely resolved question is whether neural autoassociative memories can search for approximate nearest neighbors faster than other index structures for similarity search, in particular for the case of very high dimensional vectors. ISSN 2519-2205 (Online), ISSN 0454-9910 (Print). Киб...

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