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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).
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D. A. Rachkovskij and T. V. Fedoseyeva, “On Audio Signals Recognition by Multilevel Neural Network,” in International Symposium on Neural Networks and Neural Computing (NEURONET) , 1990, pp. 281–283
1990
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P. Thagard, K. J. Holyoak, G. Nelson, and D. Gochfeld, “Analog Retrieval by Constraint Satisfaction,” Artificial Intelligence , vol. 46, no. 3, pp. 259–310, 1990
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1991
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G. A. Miller and W. G. Charles, “Contextual Correlates of Semantic Similarity,” Language and Cognitive Processes , vol. 6, no. 1, pp. 1–28, 1991
1991
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E. M. Kussul, D. A. Rachkovskij, and T. N. Baidyk, “On Image Texture Recognition by Associative-Projective Neurocomputer,” in Intelligent Engineering Systems through Artificial Neural Networks (ANNIE) , 1991, pp. 453–458
1991
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E. M. Kussul, D. A. Rachkovskij, and T. N. Baidyk, “Associative-Projective Neural Networks: Architecture, Implementation, Applications,” in International Conference on Neural Networks and Their Applications (NEURO) , 1991, pp. 463–476
1991
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E. M. Kussul and D. A. Rachkovskij, “Multilevel Assembly Neural Architecture and Processing of Sequences,” in Neurocomputers and Attention: Connectionism and Neurocomputers , vol. 2, 1991, pp. 577–590
1991
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E. M. Kussul, Associative Neuron-like Structures . Naukova Dumka (in Russian), 1992
1992
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S. I. Gallant and W. R. Caid, “MatchPlus: A Context Vector System for Document Retrieval,” in Workshop on Human Language Technology , 1993, p. 396
1993
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E. M. Kussul, T. N. Baidyk, V. V. Lukovich, and D. A. Rachkovskij, “Adaptive Neural Network Classifier with Multifloat Input Coding,” in International Conference on Neural Networks and Their Applications (NEURO) , 1993, pp. 209–216
1993
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R. Hecht-Nielsen, “Context Vectors: General Purpose Approximate Meaning Representations Self-organized from Raw Data,” Computational intelligence: Imitating Life , vol. 3, no. 11, pp. 43–56, 1994
1994
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C. M. Wharton, K. J. Holyoak, P. E. Downing, T. E. Lange, T. D. Wickens, and E. R. Melz, “Below the surface: Analogical similarity and retrieval competition in reminding,” Cognitive Psychology , vol. 26, no. 1, pp. 64–101, 1994
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T. A. Plate, “Estimating Analogical Similarity by Dot-products of Holographic Reduced Representations,” in Advances in Neural Information Processing Systems (NIPS) , 1994, pp. 1109–1116
1994
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1994
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——, “Adaptive High Performance Classifier Based on Random Threshold Neurons,” in European Meeting on Cybernetics and Systems (EMCSR) , 1994, pp. 1687–1694
1994
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I. Danihelka, G. Wayne, B. Uria, N. Kalchbrenner, and A. Graves, “Associative Long Short-term Memory,” in International Conference on Machine Learning (ICML) , 2016, pp. 1986–1994
1994
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T. A. Plate, “Holographic Reduced Representations,” IEEE Transactions on Neural Networks , vol. 6, no. 3, pp. 623–641, 1995
1995
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1995
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P. Resnik, “Using Information Content to Evaluate Semantic Similarity in a Taxonomy,” in International Joint Conference on Artificial Intelligence (IJCAI) , 1995, pp. 448–453
1995
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1995
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1995
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D. A. Rachkovskij, “Application of Stochastic Assembly Neural Networks in the Problem of Interesting Text Selection,” Neural Network Systems for Information Processing (in Russian) , pp. 52–64, 1996
1996
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K. Lund and C. Burgess, “Producing High-dimensional Semantic Spaces From Lexical Co-occurrence,” Behavior Research Methods, Instruments, & Computers , vol. 28, no. 2, pp. 203–208, 1996
1996
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A. Sato and K. Yamada, “Generalized Learning Vector Quantization,” in Advances in Neural Information Processing Systems (NIPS) , 1996, pp. 423–429
1996
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T. A. Plate, “A Common Framework for Distributed Representation Schemes for Compositional Structure,” in Connectionist Systems for Knowledge Representation and Deduction , 1997, pp. 15–34
1997
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T. K. Landauer and S. T. Dumais, “A Solution to Plato’s Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge,” Psychological Review , vol. 104, no. 2, pp. 211–240, 1997
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P. Kanerva, “Fully Distributed Representation,” in Real World Computing Symposium (RWC) , 1997, pp. 358–365
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T. A. Plate, “Structure Matching and Transformation with Distributed Representations,” in Connectionist-Symbolic Integration , 1997, pp. 1–19
1997
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J. E. Hummel and K. J. Holyoak, “Distributed Representations of Structure: A Theory of Analogical Access and Mapping,” Psychological Review , vol. 104, no. 3, pp. 427–466, 1997
1997
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E. M. Kussul, L. M. Kasatkina, D. A. Rachkovskij, and D. C. Wunsch, “Application of Random Threshold Neural Networks for Diagnostics of Micro Machine Tool Condition,” in International Joint Conference on Neural Networks (IJCNN) , vol. 1, 1998, pp. 241–244
1998
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P. Kanerva, “Dual Role of Analogy in the Design of a Cognitive Computer,” in Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational, and Neural Sciences , 1998, pp. 164–170
1998
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D. A. Rachkovskij and E. M. Kussul, “DataGen: A Generator of Datasets for Evaluation of Classification Algorithms,” Pattern Recognition Letters , vol. 19, no. 7, pp. 537–544, 1998
1998
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M. Pelillo, “Replicator Equations, Maximal Cliques, and Graph Isomorphism,” Neural Computation , vol. 11, no. 8, pp. 1933–1955, 1999
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S. I. Gallant, “Context Vectors: A Step Toward a Grand Unified Representation,” in International Workshop on Hybrid Neural Systems , ser. Lecture Notes in Computer Science, vol. 1778, 2000, pp. 204–210
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P. Kanerva, J. Kristoferson, and A. Holst, “Random Indexing of Text Samples for Latent Semantic Analysis,” in Annual Meeting of the Cognitive Science Society (CogSci) , 2000, p. 1036
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C. H. Papadimitriou, P. Raghavan, H. Tamaki, and S. Vempala, “Latent Semantic Indexing: A Probabilistic Analysis,” Journal of Computer and System Sciences , vol. 61, no. 2, pp. 217–235, 2000
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J. Neumann, “Learning Holistic Transformation of HRR from Examples,” in International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES) , 2000, pp. 557–560
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P. Kanerva, “Large Patterns Make Great Symbols: An example of Learning from Example,” in International Workshop on Hybrid Neural Systems , ser. Lecture Notes in Computer Science, vol. 1778, 2000, pp. 194–203
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T. A. Plate, “Randomly Connected Sigma-pi Neurons Can Form Associative Memories,” Network: Computation in Neural Systems , vol. 11, no. 4, pp. 321–332, 2000
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M. Sahlgren, “Vector-based Semantic Analysis: Representing Word Meanings based on Random Labels,” in ESSLI Workshop on Semantic Knowledge Acquisition and Categorization , 2001, pp. 1–21
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J. Karlgren and M. Sahlgren, “From Words to Understanding,” The Foundations of Real-World Intelligence , pp. 294–308, 2001
2001
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P. Kanerva, G. Sjödin, J. Kristoferson, R. Karlsson, B. Levin, A. Holst, J. Karlgren, and M. Sahlgren, “Computing with Large Random Patterns,” in The Foundations of Real-World Intelligence , 2001, pp. 251–272
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D. A. Rachkovskij, “Representation and Processing of Structures with Binary Sparse Distributed Codes,” IEEE Transactions on Knowledge and Data Engineering , vol. 3, no. 2, pp. 261–276, 2001
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R. W. Gayler, “Vector Symbolic Architectures Answer Jackendoff’s Challenges for Cognitive Neuroscience,” in Joint International Conference on Cognitive Science (ICCS/ASCS) , 2003, pp. 133–138
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D. A. Rachkovskij, “Some Approaches to Analogical Mapping with Structure Sensitive Distributed Representations,” Journal of Experimental and Theoretical Artificial Intelligence , vol. 16, no. 3, pp. 125–145, 2004
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I. S. Misuno, D. A. Rachkovskij, and S. V. Slipchenko, “Vector and Distributed Representations Reflecting Semantic Relatedness of Words,” Mathematical Machines and Systems. (In Russian) , vol. 3, pp. 50–66, 2005
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I. S. Misuno, D. A. Rachkovskij, S. V. Slipchenko, and A. M. Sokolov, “Searching for Text Information with the Help of Vector Representations,” Problems of Programming. (In Russian) , vol. 4, pp. 50–59, 2005
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D. A. Rachkovskij, S. V. Slipchenko, E. M. Kussul, and T. N. Baidyk, “Sparse Binary Distributed Encoding of Scalars,” Journal of Automation and Information Sciences , vol. 37, no. 6, pp. 12–23, 2005
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2021
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D. E. Kirilenko, A. K. Kovalev, E. Osipov, and A. I. Panov, “Question Answering for Visual Navigation in Human-Centered Environments,” in Mexican International Conference on Artificial Intelligence (MICAI) , 2021, pp. 31–45
2021
Closest in time.
A. K. Kovalev, M. Shaban, A. A. Chuganskaya, and A. I. Panov, “Applying Vector Symbolic Architecture and Semiotic Approach to Visual Dialog,” in International Conference on Hybrid Artificial Intelligence Systems (HAIS) , 2021, pp. 243–255
2021
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2021
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J. Karlgren and P. Kanerva, “Semantics in High-Dimensional Space,” Frontiers in Artificial Intelligence , vol. 4, pp. 1–6, 2021
2021
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C. Diao, D. Kleyko, J. M. Rabaey, and B. A. Olshausen, “Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing,” in International Joint Conference on Neural Networks (IJCNN) , 2021, pp. 1–9
2021
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D. Kleyko, M. Kheffache, E. P. Frady, U. Wiklund, and E. Osipov, “Density encoding enables resource-efficient randomly connected neural networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 8, pp. 3777–3783, 2021
2021
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E. P. Frady, D. Kleyko, and F. T. Sommer, “Variable Binding for Sparse Distributed Representations: Theory and Applications,” IEEE Transactions on Neural Networks and Learning Systems , vol. 99, no. PP, pp. 1–14, 2021
2021
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A. Rosato, M. Panella, and D. Kleyko, “Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks,” in International Joint Conference on Neural Networks (IJCNN) , 2021, pp. 1–10
2021
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A. Hernández-Cano, Y. Kim, and M. Imani, “A Framework for Efficient and Binary Clustering in High-Dimensional Space,” in Design, Automation Test in Europe Conference Exhibition (DATE) , 2021, pp. 1859–1864
2021
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2021
Closest in time.
C.-Y. Hsieh, Y.-C. Chuang, and A.-Y. A. Wu, “FL-HDC: Hyperdimensional Computing Design for the Application of Federated Learning,” in IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS) , 2021, pp. 1–5
2021
Closest in time.
A. Rosato, M. Panella, E. Osipov, and D. Kleyko, “On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks,” in International Work-Conference on Artificial Neural Networks (IWANN) , ser. Lecture Notes in Computer Science, vol. 12862, 2021, pp. 155–167
2021
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A. R. Voelker, P. Blouw, X. Choo, N. S.-Y. Dumont, T. C. Stewart, and C. Eliasmith, “Simulating and Predicting Dynamical Systems with Spatial Semantic Pointers,” Neural Computation , vol. 33, no. 8, pp. 2033–2067, 2021
2021
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2021
Closest in time.
N. McDonald, R. Davis, L. Loomis, and J. Kopra, “Aspects of Hyperdimensional Computing for Robotics: Transfer Learning, Cloning, Extraneous Sensors, and Network Topology,” in Disruptive Technologies in Information Sciences , 2021, pp. 1–14
2021
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I. Tolstikhin, N. Houlsby, A. Kolesnikov, L. Beyer, X. Zhai, T. Unterthiner, J. Yung, D. Keysers, J. Uszkoreit, M. Lucic, and A. Dosovitskiy, “MLP-Mixer: An all-MLP Architecture for Vision,” in Advances in Neural Information Processing Systems (NeurIPS) , 2021, pp. 1–12
2021
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W. Olin-Ammentorp and M. Bazhenov, “Bridge Networks: Relating Inputs through Vector-Symbolic Manipulations,” in International Conference on Neuromorphic Systems (ICONS) , 2021, pp. 1–6
2021
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T. Bricken and C. Pehlevan, “Attention Approximates Sparse Distributed Memory,” in Advances in Neural Information Processing Systems (NeurIPS) , 2021, pp. 1–15
2021
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2021
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A. Ganesan, H. Gao, S. Gandhi, E. Raff, T. Oates, J. Holt, and M. McLean, “Learning with Holographic Reduced Representations,” in Advances in Neural Information Processing Systems (NeurIPS) , 2021, pp. 1–15
2021
Closest in time.
M. Zeman, E. Osipov, and Z. Bosnic, “Compressed Superposition of Neural Networks for Deep Learning in Edge Computing,” in International Joint Conference on Neural Networks (IJCNN) , 2021, pp. 1–8
2021
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2021
Closest in time.
2021
Closest in time.
D. Kleyko, D. A. Rachkovskij, E. Osipov, and A. Rahimi, “A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part I: Models and Data Transformations,” ACM Computing Surveys , vol. 55, no. 6, pp. 1–40, 2022
2022
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P. beim Graben, M. Huber, W. Meyer, R. Romer, C. Tschope, and M. Wolff, “Vector Symbolic Architectures for Context-Free Grammars,” Cognitive Computation , vol. 14, pp. 733–748, 2022
2022
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A. Menon, A. Natarajan, L. I. G. Olascoaga, Y. Kim, B. Benedict, and J. M. Rabaey, “On the Role of Hyperdimensional Computing for Behavioral Prioritization in Reactive Robot Navigation Tasks,” in IEEE International Conference on Robotics and Automation (ICRA) , 2022, pp. 1–7
2022
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2022
Closest in time.
2022
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R. Guirado, A. Rahimi, G. Karunaratne, E. Alarcón, A. Sebastian, and S. Abadal, “Wireless On-Chip Communications for Scalable In-memory Hyperdimensional Computing,” in International Joint Conference on Neural Networks (IJCNN) , 2022, pp. 1–8
2022
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I. Barclay, C. Simpkin, G. Bent, T. L. Porta, D. Millar, A. Preece, I. Taylor, and D. Verma, “Trustable Service Discovery for Highly Dynamic Decentralized Workflows,” Future Generation Computer Systems , pp. 1–12, 2022
2022
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D. Kleyko, C. Bybee, C. J. Kymn, B. A. Olshausen, A. Khosrowshahi, D. E. Nikonov, F. T. Sommer, and E. P. Frady, “Integer Factorization with Compositional Distributed Representations,” in Neuro-Inspired Computational Elements Conference (NICE) , 2022, pp. 73–80
2022
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——, “Computing on Functions Using Randomized Vector Representations (in brief),” in Neuro-Inspired Computational Elements Conference (NICE) , 2022, pp. 115–122
2022
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D. A. Rachkovskij, “Representation of Spatial Objects by Shift-equivariant Similarity-Preserving Hypervectors,” Neural Computing and Applications , vol. 34, no. 24, pp. 22 387–22 403, 2022
2022
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2022
Closest in time.
Y. Ma and B. Ma, “Multimodal Sentiment Analysis on Unaligned Sequences Via Holographic Embedding,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2022, pp. 8547–8551
2022
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Y. Yao, W. Liu, G. Zhang, and W. Hu, “Radar-Based Human Activity Recognition Using Hyperdimensional Computing,” IEEE Transactions on Microwave Theory and Techniques , vol. 70, no. 3, pp. 1605–1619, 2022
2022
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A. Menon, A. Natarajan, R. Agashe, D. Sun, M. Aristio, H. Liew, Y. S. Shao, and J. M. Rabaey, “Efficient Emotion Recognition Using Hyperdimensional Computing with Combinatorial Channel Encoding and Cellular Automata,” Brain Informatics , vol. 9, pp. 1–13, 2022
2022
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2022
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U. Pale, T. Teijeiro, and D. Atienza, “Multi-Centroid Hyperdimensional Computing Approach for Epileptic Seizure Detection,” Frontiers in Neurology , vol. 13, pp. 1–13, 2022
2022
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——, “Applicability of Hyperdimensional Computing to Seizure Detection,” IEEE Open Journal of Circuits and Systems , vol. 3, pp. 59–71, 2022
2022
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D. Kleyko, G. Karunaratne, J. M. Rabaey, A. Sebastian, and A. Rahimi, “Generalized key-value memory to flexibly adjust redundancy in memory-augmented networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 99, no. PP, pp. 1–6, 2022
2022
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M. Hersche, G. Karunaratne, G. Cherubini, L. Benini, A. Sebastian, and A. Rahimi, “Constrained Few-shot Class-incremental Learning,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 1–19
2022
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I. Nunes, M. Heddes, T. Givargis, A. Nicolau, and A. Veidenbaum, “GraphHD: Efficient Graph Classification using Hyperdimensional Computing,” in Design, Automation and Test in Europe Conference (DATE) , 2022, pp. 1485–1490
2022
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2022
Closest in time.
D. A. Rachkovskij and D. Kleyko, “Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences,” in International Joint Conference on Neural Networks (IJCNN) , 2022, pp. 1–8
2022
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H. R. Schmidtke and S.Coelho, “Scales and Hedges in a Logic with Analogous Semantics,” in Annual Conference on Advances in Cognitive Systems (ACS) , 2022, pp. 1–20
2022
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2022
Closest in time.
K. Schlegel, P. Neubert, and P. Protzel, “HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing,” in International Joint Conference on Neural Networks (IJCNN) , 2022, pp. 1–8
2022
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2022
Closest in time.
P. M. Furlong, T. C. Stewart, and C. Eliasmith, “Fractional Binding in Vector Symbolic Representations for Efficient Mutual Information Exploration,” in ICRA Workshop: Towards Curious Robots: Modern Approaches for Intrinsically-Motivated Intelligent Behavior , 2022, pp. 1–5
2022
Closest in time.
P. M. Furlong and C. Eliasmith, “Fractional Binding in Vector Symbolic Architectures as Quasi-Probability Statements,” in Annual Meeting of the Cognitive Science Society (CogSci) , 2022, pp. 259–266
2022
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N. S.-Y. Dumont, J. Orchard, and C. Eliasmith, “A Model of Path Integration that Connects Neural and Symbolic Representation,” in Annual Meeting of the Cognitive Science Society (CogSci) , 2022, pp. 3662–3668
2022
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M. Bartlett, T. C. Stewart, and J. Orchard, “Biologically-Based Neural Representations Enable Fast Online Shallow Reinforcement Learning,” in Annual Meeting of the Cognitive Science Society (CogSci) , 2022, pp. 2981–2987
2022
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2022
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L. I. G. Olascoaga, A. Menon, M. Ibrahim, and J. M. Rabaey, “A Brain-Inspired Hierarchical Reasoning Framework for Cognition-Augmented Prosthetic Grasping,” in AAAI Workshop on Combining Learning and Reasoning , 2022, pp. 1–9
2022
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D. Kleyko, E. P. Frady, M. Kheffache, and E. Osipov, “Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware,” IEEE Transactions on Neural Networks and Learning Systems , vol. 33, no. 4, pp. 1688–1701, 2022
2022
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G. Bent, C. Simpkin, Y. Li, and A. Preece, “Hyperdimensional Computing using Time-to-spike Neuromorphic Circuits,” in International Joint Conference on Neural Networks (IJCNN) , 2022, pp. 1–8
2022
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Z. Zou, H. Alimohamadi, A. Zakeri, F. Imani, Y. Kim, M. H. Najafi, and M. Imani, “Memory-inspired Spiking Hyperdimensional Network for Robust Online Learning,” Scientific Reports , vol. 12, pp. 1–13, 2022
2022
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J. Morris, H. W. Lui, K. Stewart, B. Khaleghi, A. Thomas, T. Marback, B. Aksanli, E. Neftci, and T. Rosing, “HyperSpike: HyperDimensional Computing for More Efficient and Robust Spiking Neural Networks,” in Design, Automation and Test in Europe Conference (DATE) , 2022, pp. 664–669
2022
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2022
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2022
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A. Renner, Y. Sandamirskaya, F. T. Sommer, and E. P. Frady, “Sparse Vector Binding on Spiking Neuromorphic Hardware Using Synaptic Delays,” in International Conference on Neuromorphic Systems (ICONS) , 2022, pp. 1–5
2022
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B. Komer, T. C. Stewart, A. R. Voelker, and C. Eliasmith, “A Neural Representation of Continuous Space using Fractional Binding,” in Annual Meeting of the Cognitive Science Society (CogSci) , 2019, pp. 2038–2043
2043
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