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Motivated by recent innovations in biologically-inspired neuromorphic hardware, this article presents a novel unsupervised machine learning algorithm named Hyperseed that draws on the principles of Vector Symbolic Architectures (VSA) for fast learning of a topology preserving feature map of unlabelled data.
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2017
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D. A. Rachkovskij, “Distance-Based Index Structures for Fast Similarity Search,” Cybernetics and Systems Analysis , vol. 53, pp. 636–658, 2017
2017
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H.-S. Kim, “HDM: Hyper-Dimensional Modulation for Robust Low-Power Communications,” in IEEE International Conference on Communications (ICC) , 2018, pp. 1–6
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D. Kleyko, A. Rahimi, D. A. Rachkovskij, E. Osipov, and J. M. Rabaey, “Classification and Recall with Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristic,” IEEE Transactions on Neural Networks and Learning Systems , vol. 29, no. 12, pp. 5880–5898, 2018
2018
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D. Kleyko, E. Osipov, N. Papakonstantinou, and V. Vyatkin, “Hyperdimensional Computing in Industrial Systems: The Use-Case of Distributed Fault Isolation in a Power Plant,” IEEE Access , vol. 6, pp. 30 766–30 777, 2018
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E. P. Frady, D. Kleyko, and F. T. Sommer, “A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks,” Neural Computation , vol. 30, pp. 1449–1513, 2018
2018
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A. Rahimi, P. Kanerva, L. Benini, and J. M. Rabaey, “Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals,” Proceedings of the IEEE , vol. 107, no. 1, pp. 123–143, 2019
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