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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 Symbolic Architecture (VSA), and, synonymously, Hyperdimensional (HD) computing.
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Vector Symbolic Architectures Answer Jackendoff’s Challenges for Cognitive Neuroscience
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An improved data stream summary: The count-min sketch and its applications
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High-dimensional computing with sparse vectors
Laiho, M., Poikonen, J. H., Kanerva, P., and Lehtonen, E. (2015) · 2015
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Encoding sequential information in semantic space models: Comparing holographic reduced representation and random permutation
Recchia, G., Sahlgren, M., Kanerva, P., and Jones, M. N. (2015) · 2015
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Reasoning with vectors: A continuous model for fast robust inference
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Concepts as semantic pointers: A framework and computational model
Blouw, P., Solodkin, E., Thagard, P., and Eliasmith, C. (2016) · 2016
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Why neurons mix: high dimensionality for higher cognition
Fusi, S., Miller, E. K., and Rigotti, M. (2016) · 2016
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Sequence prediction with sparse distributed hyperdimensional coding applied to the analysis of mobile phone use patterns
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Rachkovskij, D. A., Slipchenko, S. V., Kussul, E. M., and Baidyk, T. N. (2005) · 2005
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Donoho, D. L. (2006) · 2006
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Bayesian inference with probabilistic population codes
Ma, W. J., Beck, J. M., Latham, P. E., and Pouget, A. (2006) · 2006
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Estimation of a quadratic regression functional using the sinc kernel
Bissantz, N. and Holzmann, H. (2007) · 2007
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Speckle phenomena in optics: theory and applications
Goodman, J. W. (2007) · 2007
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Räsänen, O. and Saarinen, J. (2016) · 2016
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A neural architecture for representing and reasoning about spatial relationships
Weiss, E., Cheung, B., and Olshausen, B. A. (2016) · 2016
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A novel nonparametric maximum likelihood estimator for probability density functions
Agarwal, R., Chen, Z., and Sarma, S. V. (2017) · 2017
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Dasgupta, S., Stevens, C. F., and Navlakha, S. (2017) · 2017
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Neural distributed autoassociative memories: A survey
Gritsenko, V. I., Rachkovskij, D. A., Frolov, A. A., Gayler, R. W., Kleyko, D., and Osipov, E. (2017) · 2017
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Probability and computing: Randomization and probabilistic techniques in algorithms and data analysis
Mitzenmacher, M. and Upfal, E. (2017) · 2017
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Associative synthesis of finite state automata model of a controlled object with hyperdimensional computing
Osipov, E., Kleyko, D., and Legalov, A. (2017) · 2017
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High-dimensional computing as a nanoscalable paradigm
Rahimi, A., Datta, S., Kleyko, D., Frady, E. P., Olshausen, B., Kanerva, P., and Rabaey, J. M. (2017) · 2017
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Computing with randomness
Alaghi, A. and Hayes, J. P. (2018) · 2018
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Thermometer encoding: One hot way to resist adversarial examples
Buckman, J., Roy, A., Raffel, C., and Goodfellow, I. (2018) · 2018
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Blessing of dimensionality: Mathematical foundations of the statistical physics of data
Gorban, A. N. and Tyukin, I. Y. (2018) · 2018
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Neural tangent kernel: Convergence and generalization in neural networks
Jacot, A., Gabriel, F., and Hongler, C. (2018) · 2018
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The hyperdimensional stack machine
Yerxa, T., Anderson, A., and Weiss, E. (2018) · 2018
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Robust computation with rhythmic spike patterns
Frady, E. P. and Sommer, F. T. (2019) · 2019
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Vector-space models of semantic representation from a cognitive perspective: A discussion of common misconceptions
Günther, F., Rinaldi, L., and Marelli, M. (2019) · 2019
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Representing spatial relations with fractional binding
Lu, T., Voelker, A. R., Komer, B., and Eliasmith, C. (2019) · 2019
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An introduction to hyperdimensional computing for robotics
Neubert, P., Schubert, S., and Protzel, P. (2019) · 2019
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Efficient biosignal processing using hyperdimensional computing: Network templates for combined learning and classification of ExG signals
Rahimi, A., Kanerva, P., Benini, L., and Rabaey, J. M. (2019) · 2019
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Accurate representation for spatial cognition using grid cells
Dumont, N. S.-Y. and Eliasmith, C. (2020) · 2020
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Resonator networks, 1: An efficient solution for factoring high-dimensional, distributed representations of data structures
Frady, E. P., Kent, S. J., Olshausen, B. A., and Sommer, F. T. (2020) · 2020
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Differential geometry and Lie groups: a computational perspective
Gallier, J. and Quaintance, J. (2020) · 2020
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Biologically Inspired Spatial Representation
Komer, B. (2020) · 2020
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Efficient navigation using a scalable, biologically inspired spatial representation
Komer, B. and Eliasmith, C. (2020) · 2020
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Bach, F. (2019) · 2021
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Variable binding for sparse distributed representations: Theory and applications
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Vector symbolic architectures as a computing framework for nanoscale hardware
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