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Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is a framework for computing with distributed representations by exploiting properties of random high-dimensional vector spaces.
Neural networks and physical systems with emergent collective computational abilities
John J Hopfield · 1982
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Am I thinking assemblies?
Christoph von der Malsburg · 1986
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Connectionism and cognitive architecture: A critical analysis
Jerry A Fodor and Zenon W Pylyshyn · 1988
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Sparse distributed memory
Pentti Kanerva · 1988
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Tensor product variable binding and the representation of symbolic structures in connectionist systems
Paul Smolensky · 1990
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Associative-projective neural networks: Architecture, implementation, applications
Ernst M Kussul, Dmitri A Rachkovskij, and Tatyana N Baidyk · 1991
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Holographic reduced representations: Convolution algebra for compositional distributed representations
Tony A Plate · 1991
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Estimating analogical similarity by dot-products of holographic reduced representations
Tony A Plate · 1994
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Holographic reduced representations
Tony A Plate · 1995
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Fully distributed representation
Pentti Kanerva · 1997
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Multiplicative binding, representation operators & analogy
Ross W Gayler · 1998
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Large patterns make great symbols: An example of learning from example
Pentti Kanerva · 2000
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Vector symbolic architectures answer Jackendoff’s challenges for cognitive neuroscience
Ross W Gayler · 2003
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Sparse binary distributed encoding of scalars
Dmitri A Rachkovskij, Sergey V Slipchenko, Ernst M Kussul, and Tatyana N Baidyk · 2005
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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A distributed basis for analogical mapping: New frontiers in analogy research
Ross W Gayler and Simon D Levy · 2009
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Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors
Pentti Kanerva · 2009
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Similarity-based retrieval with structure-sensitive sparse binary distributed representations
Dmitri A Rachkovskij and Serge V Slipchenko · 2012
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Analogical mapping and inference with binary spatter codes and sparse distributed memory
Blerim Emruli, Ross W Gayler, and Fredrik Sandin · 2013
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Do we need hundreds of classifiers to solve real world classification problems?
Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, and Dinani Amorim · 2014
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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A spiking neural model applied to the study of human performance and cognitive decline on Raven’s advanced progressive matrices
Daniel Rasmussen and Chris Eliasmith · 2014
Cited alongside, same era.
Imitation of honey bees’ concept learning processes using vector symbolic architectures
Denis Kleyko, Evgeny Osipov, Ross W Gayler, Asad I Khan, and Adrian G Dyer · 2015
Cited alongside, same era.
High-dimensional computing with sparse vectors
Mika Laiho, Jussi H Poikonen, Pentti Kanerva, and Eero Lehtonen · 2015
Cited alongside, same era.
Dense associative memory for pattern recognition
Dmitry Krotov and John J Hopfield · 2016
Cited alongside, same era.
UCI machine learning repository, 2017
Dheeru Dua and Casey Graff · 2017
Cited alongside, same era.
VoiceHD: Hyperdimensional computing for efficient speech recognition
Mohsen Imani, Deqian Kong, Abbas Rahimi, and Tajana Rosing · 2017
Learning with holographic reduced representations
Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates, James Holt, and Mark McLean · 2021
Later among the works it cites.
OnlineHD: Robust, efficient, and single-pass online learning using hyperdimensional system
Alejandro Hernandez-Cane, Namiko Matsumoto, Eric Ping, and Mohsen Imani · 2021
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Density encoding enables resource-efficient randomly connected neural networks
Denis Kleyko, Mansour Kheffache, E Paxon Frady, Urban Wiklund, and Evgeny Osipov · 2021
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Theoretical foundations of hyperdimensional computing
Anthony Thomas, Sanjoy Dasgupta, and Tajana Rosing · 2021
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Computing on functions using randomized vector representations (in brief)
E Paxon Frady, Denis Kleyko, Christopher J Kymn, Bruno A Olshausen, and Friedrich T Sommer · 2022
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Hyperdimensional hashing: A robust and efficient dynamic hash table
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Cited alongside, same era.
Associative synthesis of finite state automata model of a controlled object with hyperdimensional computing
Evgeny Osipov, Denis Kleyko, and Alexander Legalov · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
A theory of sequence indexing and working memory in recurrent neural networks
E Paxon Frady, Denis Kleyko, and Friedrich T Sommer · 2018
Cited alongside, same era.
The hyperdimensional stack machine
Thomas Yerxa, Alexander Anderson, and Eric Weiss · 2018
Cited alongside, same era.
Vector-derived transformation binding: An improved binding operation for deep symbol-like processing in neural networks
Jan Gosmann and Chris Eliasmith · 2019
Cited alongside, same era.
Mike Heddes, Igor Nunes, Tony Givargis, Alexandru Nicolau, and Alex Veidenbaum · 2022
Closest in time.
Constrained few-shot class-incremental learning
Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi · 2022
Closest in time.
OpenHD: A GPU-powered framework for hyperdimensional computing
Jaeyoung Kang, Behnam Khaleghi, Tajana Rosing, and Yeseong Kim · 2022
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GraphHD: Efficient graph classification using hyperdimensional computing
Igor Nunes, Mike Heddes, Tony Givargis, Alexandru Nicolau, and Alex Veidenbaum · 2022
Closest in time.
Neuromorphic visual scene understanding with resonator networks
Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, Bruno A Olshausen, Yulia Sandamirskaya, Friedrich T Sommer, and E Paxon Frady · 2022
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A comparison of vector symbolic architectures
Kenny Schlegel, Peer Neubert, and Peter Protzel · 2022
Closest in time.
HDTorch: Accelerating hyperdimensional computing with GP-GPUs for design space exploration
William Andrew Simon, Una Pale, Tomas Teijeiro, and David Atienza · 2022
Closest in time.
Neurocompositional computing: From the central paradox of cognition to a new generation of AI systems
Paul Smolensky, Richard McCoy, Roland Fernandez, Matthew Goldrick, and Jianfeng Gao · 2022
Closest in time.
On separating long-and short-term memories in hyperdimensional computing
Jeffrey L Teeters, Denis Kleyko, Pentti Kanerva, and Bruno A Olshausen · 2022
Closest in time.
Capacity analysis of vector symbolic architectures
Kenneth L Clarkson, Shashanka Ubaru, and Elizabeth Yang · 2023
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Variable binding for sparse distributed representations: Theory and applications
E Paxon Frady, Denis Kleyko, and Friedrich T. Sommer · 2023
Closest in time.
A neuro-vector-symbolic architecture for solving Raven’s progressive matrices
Michael Hersche, Mustafa Zeqiri, Luca Benini, Abu Sebastian, and Abbas Rahimi · 2023
Closest in time.
A survey on hyperdimensional computing aka vector symbolic architectures, part II: Applications, cognitive models, and challenges
Denis Kleyko, Dmitri A Rachkovskij, Evgeny Osipov, and Abbas Rahimi · 2023
Closest in time.
In-memory factorization of holographic perceptual representations
Jovin Langenegger, Geethan Karunaratne, Michael Hersche, Luca Benini, Abu Sebastian, and Abbas Rahimi · 2023
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An extension to basis-hypervectors for learning from circular data in hyperdimensional computing
Igor Nunes, Mike Heddes, Tony Givargis, and Alexandru Nicolau · 2023
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