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20152026
most citedA Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

147 citations · 702 across the 33 of their papers we have counts for

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Showing 2021Show all

9 papers · 1 filter

cs.AI2021★ 147 cited

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

Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov +1

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 Architectur…

cs.CL2021★ 25 cited

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…

cs.AI2021★ 141 cited

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

Denis Kleyko, Dmitri A. Rachkovskij, Evgeny Osipov +1

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

cs.AI2021

Hyperseed: Unsupervised Learning with Vector Symbolic Architectures

Evgeny Osipov, Sachin Kahawala, Dilantha Haputhanthri +4

Motivated by recent innovations in biologically-inspired neuromorphic hardware, this article presents a novel unsupervised machine learning algorithm named Hyperseed that draws on…

cs.LG2021★ 5 cited

Computing on Functions Using Randomized Vector Representations

E. Paxon Frady, Denis Kleyko, Christopher J. Kymn +2

Vector space models for symbolic processing that encode symbols by random vectors have been proposed in cognitive science and connectionist communities under the names Vector Symbo…

cs.LG2021★ 5 cited

On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks

Antonello Rosato, Massimo Panella, Evgeny Osipov +1

A change of the prevalent supervised learning techniques is foreseeable in the near future: from the complex, computational expensive algorithms to more flexible and elementary tra…