Search Results for author: Dmitri A. Rachkovskij

Found 5 papers, 0 papers with code

Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences

no code implementations27 Jan 2022 Dmitri A. Rachkovskij, Denis Kleyko

Hyperdimensional computing (HDC), also known as vector symbolic architectures (VSA), is a computing framework used within artificial intelligence and cognitive computing that operates with distributed vector representations of large fixed dimensionality.

Word Similarity

Shift-Equivariant Similarity-Preserving Hypervector Representations of Sequences

no code implementations31 Dec 2021 Dmitri A. Rachkovskij

However, they can be adapted to hypervectors in formats of other HDC/VSA models, as well as for representing sequences of types other than symbolic strings.

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

no code implementations12 Nov 2021 Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov, Abbas Rahimi

This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA).

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations

no code implementations11 Nov 2021 Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov, Abbas Rahimi

Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations.

Electrical Engineering

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