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Denis Kleyko

Position: Associate Senior Lecturer School/office: School of Science and Technology

Email: ZGVuaXMua2xleWtvO29ydS5zZQ==

Phone: No number available

Room: T2227

Research subject

Computer Science

Research environments

Centre for Applied Autonomous Sensor Systems (AASS)

About Denis Kleyko

Denis Kleyko is an Assistant Professor in Computer Science at Örebro University. Denis obtained his Ph.D. in Computer Science in 2018 from Luleå University of Technology. Prior to joining Örebro University, he has completed three postdoctoral stays at different instritutions. The first stay was at the Department of Computer Science, Electrical and Space Engineering at Luleå University of Technology (2018-2019), the second one was at the Redwood Center for Theoretical Neuroscience at University of California at Berkeley (2020-2022), and the third one was at Intelligent Systems Lab at Research Institutes of Sweden (2022-2023). The last two stays were a part of the Marie Skłodowska-Curie Global Individual Fellowship that Denis was awarded in 2019.

Denis’s fascination for research is in computing with randomness that manifistates in how complex can be the functionality of seemingly simple algorithms that rely on some form of randomness. In particular, he is interested in how could randomness benefit algorithms within artificial intelligence and machine learning. One of his primary interests is a computing framework of vector symbolic architectures, which is also known as hyperdimensional computing, which exploits randomness for knowledge representation, computing, learning, and reasoning. He seeks to understant how this framework could be connected to emerging low-power computing hardware and how could it enable the design of novel architectures for artificial neural networks. Broadly, his research interests also include numerous information processing methods both brain- and physics-inspired such as reservoir computing, associative memories, prototype-based learning, cellular automata computations, sparse coding, kernel-based methods, Ising machines, sketching techniques, self-organizing maps, and formation of similarity-preserving embeddigns.

As a part of community service, Denis is a webmaster and an author of a website dedicated to hyperdimensional computing – www.hd-computing.com. He also helps organizing regular series of webinars on this topic – “Online Speakers’ Corner on Vector Symbolic Architectures and Hyperdimensional Computing”.

Denis is an IEEE Senior Member, a Member of the European Laboratory for Learning and Intelligent Systems (ELLIS Society), and a Member of the Marie Curie Alumni Association.

Research groups
Publications

Research groups

Machine Perception and Interaction Lab

Publications

Articles in journals

Kymn, C. J. , Kleyko, D. , Frady, E. P. , Bybee, C. , Kanerva, P. , Sommer, F. T. & Olshausen, B. A. (2024). Computing With Residue Numbers in High-Dimensional Representation. Neural Computation. [BibTeX]
Osipov, E. , Kahawala, S. , Haputhanthri, D. , Kempitiya, T. , De Silva, D. , Alahakoon, D. & Kleyko, D. (2024). Hyperseed: Unsupervised learning with vector symbolic architectures. IEEE Transactions on Neural Networks and Learning Systems, 35 (5), 6583-6597. [BibTeX]
Schlegel, K. , Kleyko, D. , Brinkmann, B. H. , Nurse, E. S. , Gayler, R. W. & Neubert, P. (2024). Lessons from a challenge on forecasting epileptic seizures from non-cerebral signals. Nature Machine Intelligence, 6 (2), 243-244. [BibTeX]
Kleyko, D. , Rosato, A. , Paxon Frady, E. , Panella, M. & Sommer, F. T. (2024). Perceptron Theory Can Predict the Accuracy of Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 35 (7), 9885-9899. [BibTeX]
Kleyko, D. , Rachkovskij, D. A. , Osipov, E. & Rahimi, A. (2023). A survey on hyperdimensional computing aka vector symbolic architectures: Part I: Models and data transformations. ACM Computing Surveys, 55 (6). [BibTeX]
Srivastava, A. , Rastogi, A. & Kleyko, D. (2023). Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. Transactions on Machine Learning Research, 5, 1-95. [BibTeX]
Kleyko, D. , Bybee, C. , Huang, P. , Kymn, C. J. , Olshausen, B. A. , Frady, E. P. & Sommer, F. T. (2023). Efficient decoding of compositional structure in holistic representations. Neural Computation, 35 (7), 1159-1186. [BibTeX]
Bybee, C. , Kleyko, D. , Nikonov, D. E. , Khosrowshahi, A. , Olshausen, B. A. & Sommer, F. T. (2023). Efficient optimization with higher-order ising machines. Nature Communications, 14 (1). [BibTeX]
Kleyko, D. , Karunaratne, G. , Rabaey, J. M. , Sebastian, A. & Rahimi, A. (2023). Generalized key-value memory to flexibly adjust redundancy in memory-augmented networks. IEEE Transactions on Neural Networks and Learning Systems, 34 (12), 10993-10998. [BibTeX]
Dhole, K. , Kleyko, D. & Zhang, Y. (2023). NL-augmenter: A framework for task-sensitive natural language augmentation. Northern European Journal of Language Technology (NEJLT), 9 (1), 1-41. [BibTeX]
Teeters, J. L. , Kleyko, D. , Kanerva, P. & Olshausen, B. A. (2023). On separating long- and short-term memories in hyperdimensional computing. Frontiers in Neuroscience, 16. [BibTeX]
Heddes, M. , Nunes, I. , Vergés, P. , Kleyko, D. , Abraham, D. , Givargis, T. , Nicolau, A. & Veidenbaum, A. (2023). Torchhd: An open source python library to support research on hyperdimensional computing and vector symbolic architectures. Journal of machine learning research, 24 (255), 1-10. [BibTeX]
Frady, E. P. , Kleyko, D. & Sommer, F. T. (2023). Variable binding for sparse distributed representations: theory and applications. IEEE Transactions on Neural Networks and Learning Systems, 34 (5), 2191-2204. [BibTeX]
Kleyko, D. , Frady, E. & Sommer, F. (2022). Cellular automata can reduce memory requirements of collective-state computing. IEEE Transactions on Neural Networks and Learning Systems, 33 (6), 2701-2713. [BibTeX]
Huang, P. , Kleyko, D. , Rabaey, J. M. , Olshausen, B. A. & Kanerva, P. (2022). Computing with hypervectors for efficient speaker identification. , 1-5. [BibTeX]
Kleyko, D. , Frady, E. , Kheffache, M. & Osipov, E. (2022). Integer echo state networks: efficient reservoir computing for digital hardware. IEEE Transactions on Neural Networks and Learning Systems, 33 (4), 1688-1701. [BibTeX]
Kleyko, D. , Davies, M. , Frady, E. P. , Kanerva, P. , Kent, S. J. , Olshausen, B. A. , Osipov, E. , Rabaey, J. M. & et al. (2022). Vector symbolic architectures as a computing framework for emerging hardware. Proceedings of the IEEE, 110 (10), 1538-1571. [BibTeX]
Paxon Frady, E. , Kleyko, D. , Kymn, C. J. , Olshausen, B. A. & Sommer, F. T. (2021). Computing on Functions Using Randomized Vector Representations. . [BibTeX]
Kleyko, D. , Kheffache, M. , Frady, E. P. , Wiklund, U. & Osipov, E. (2021). Density encoding enables resource-efficient randomly connected neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32 (8), 3777-3783. [BibTeX]
Kleyko, D. , Osipov, E. & Wiklund, U. (2020). A comprehensive study of complexity and performance of automatic detection of atrial fibrillation: Classification of long ECG recordings based on the PhysioNet computing in cardiology challenge 2017. Biomedical Engineering & Physics Express, 6 (2). [BibTeX]
Rutqvist, D. , Kleyko, D. & Blomstedt, F. (2020). An automated machine learning approach for smart waste management systems. IEEE Transactions on Industrial Informatics, 16 (1), 384-392. [BibTeX]
Kleyko, D. , Rahimi, A. , Gayler, R. W. & Osipov, E. (2020). Autoscaling bloom filter: controlling trade-off between true and false positives. Neural Computing & Applications, 32 (8), 3675-3684. [BibTeX]
Kleyko, D. , Gayler, R. W. & Osipov, E. (2020). Commentaries on "Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception" [Science Robotics Vol. 4 Issue 30 (2019) 1-10. . [BibTeX]
Kleyko, D. , Osipov, E. & Wiklund, U. (2019). A hyperdimensional computing framework for analysis of cardiorespiratory synchronization during paced deep breathing. IEEE Access, 7, 34403-34415. [BibTeX]
Krasheninnikov, P. V. , Melent’ev, O. G. , Kleyko, D. & Shapin, A. G. (2019). Parameter estimation for the resulting logical channel formed by minimizing channel switching. Automation and remote control, 80 (2), 278-285. [BibTeX]
Lyamin, N. , Kleyko, D. , Delooz, Q. & Vinel, A. (2019). Real-Time jamming DoS Detection in Safety-Critical V2V C-ITS using data mining. IEEE Communications Letters, 23 (3), 442-445. [BibTeX]
Frady, E. P. , Kleyko, D. & Sommer, F. T. (2018). A theory of sequence indexing and working memory in recurrent neural networks. Neural Computation, 30 (6), 1449-1513. [BibTeX]
Lyamin, N. , Kleyko, D. , Delooz, Q. & Vinel, A. (2018). AI-based malicious network traffic detection in VANETs. IEEE Network, 32 (6), 15-21. [BibTeX]
Kleyko, D. , Rahimi, A. , Rachkovskij, D. A. , Osipov, E. & Rabaey, J. M. (2018). Classification and recall with binary hyperdimensional computing: Tradeoffs in choice of density and nmapping characteristics. IEEE Transactions on Neural Networks and Learning Systems, 29 (12), 5880-5898. [BibTeX]
Kleyko, D. , Osipov, E. , Papakonstantinou, N. & Vyatkin, V. (2018). Hyperdimensional computing in industrial systems: The use-case of distributed fault isolation in a power plant. IEEE Access, 6, 30766-30777. [BibTeX]
Wedekind, D. , Kleyko, D. , Osipov, E. , Malberg, H. , Zaunseder, S. & Wiklund, U. (2018). Robust methods for automated selection of cardiac signals after blind source separation. IEEE Transactions on Biomedical Engineering, 65 (10), 2248-2258. [BibTeX]
Rahimi, A. , Datta, S. , Kleyko, D. , Frady, E. P. , Olshausen, B. , Kanerva, P. & Rabaey, J. M. (2017). High-Dimensional Computing as a Nanoscalable Paradigm. IEEE Transactions on Circuits and Systems Part 1: Regular Papers, 64 (9), 2508-2521. [BibTeX]
Kleyko, D. , Osipov, E. , Senior, A. , Khan, A. I. & Sekercioglu, Y. A. (2017). Holographic graph neuron: A bioinspired architecture for pattern processing. IEEE Transactions on Neural Networks and Learning Systems, 28 (6), 1250-1262. [BibTeX]
Grytsenko, V. I. , Rachkovskij, D. A. , Frolov, A. A. , Gayler, R. , Kleyko, D. & Osipov, E. (2017). Neural distributed autoassociative memories: A survey. Cybernetics and Computer Engineering, 188 (2), 5-35. [BibTeX]
Osipov, E. , Kleyko, D. & Shapin, A. (2016). An approach for self-adaptive path loss modelling for positioning in underground environments. International Journal of Antennas and Propagation. [BibTeX]
Kleyko, D. , Osipov, E. , Gayler, R. W. , Khan, A. I. & Dyer, A. G. (2015). Imitation of honey bees' concept learning processes using Vector Symbolic Architectures. Biologically Inspired Cognitive Architectures, 14, 57-72. [BibTeX]
Balasubramaniam, S. , Lyamin, N. , Kleyko, D. , Skurnik, M. , Vinel, A. & Koucheryavy, Y. (2014). Exploiting bacterial properties for multi-hop nanonetworks. IEEE Communications Magazine, 52 (7), 184-191. [BibTeX]
Melent’ev, O. G. & Kleyko, D. (2013). Computing the parameters of the discrete channel resulting under frequency hopping in the general case. Automation and remote control, 74 (7), 1128-1131. [BibTeX]

Conference papers

Kymn, C. J. , Mazelet, S. , Ng, A. , Kleyko, D. & Olshausen, B. A. (2024). Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks. In: 2024 Neuro Inspired Computational Elements Conference (NICE). Paper presented at Neuro Inspired Computational Elements Conference (NICE), La Jolla, CA, USA, April 23-26, 2024. IEEE. [BibTeX]
Frady, E. P. , Kleyko, D. , Kymn, C. J. , Olshausen, B. A. & Sommer, F. T. (2022). Computing on functions using randomized vector representations (in brief). In: NICE '22 Proceedings of the 2022 Annual Neuro-Inspired Computational Elements Conference. Paper presented at Annual Neuro-Inspired Computational Elements Conference, Nice, France, March 28 - April, 1, 2022. (pp. 115-122). Association for Computing Machinery. [BibTeX]
Rosato, A. , Panella, M. , Osipov, E. & Kleyko, D. (2022). Few-shot federated learning in randomized neural networks via hyperdimensional computing. In: 2022 International Joint Conference on Neural Networks (IJCNN) Proceedings. Paper presented at The International Joint Conference on Neural Networks (IJCNN 2022), Padua, Italy, July 18-23, 2022. IEEE. [BibTeX]
Kleyko, D. , Bybee, C. , Kymn, C. , Olshausen, B. , Khosrowshahi, A. , Nikonov, D. E. , Sommer, F. T. & Frady, E. P. (2022). Integer factorization with compositional distributed representations. In: NICE '22 Proceedings of the 2022 Annual Neuro-Inspired Computational Elements Conference. Paper presented at 2022 Annual Neuro-Inspired Computational Elements Conference, Nice, France, March 28 - April 1, 2022. (pp. 73-80). Association for Computing Machinery. [BibTeX]
Rachkovskij, D. A. & Kleyko, D. (2022). Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences. In: 2022 International Joint Conference on Neural Networks (IJCNN) Proceedings. Paper presented at The International Joint Conference on Neural Networks, (IJCNN 2022), Padua, Italy, July 18-23, 2022. IEEE. [BibTeX]
Diao, C. , Kleyko, D. , Rabaey, J. M. & Olshausen, B. A. (2021). Generalized learning vector quantization for classification in randomized neural networks and hyperdimensional computing. In: 2021 International Joint Conference on Neural Networks (IJCNN). Paper presented at International Joint Conference on Neural Networks (IJCNN 2021), Virtual, Shenzhen, July 18-22, 2021. (pp. 1-9). IEEE. [BibTeX]
Rosato, A. , Panella, M. & Kleyko, D. (2021). Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks. In: 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings. Paper presented at The International Joint Conference on Neural Networks, (IJCNN 2021), Virtual, Shenzhen, July 18-22, 2021. IEEE. [BibTeX]
Alonso, P. , Shridhar, K. , Kleyko, D. , Osipov, E. & Liwicki, M. (2021). HyperEmbed: Tradeoffs between resources and performance in NLP Tasks with hyperdimensional computing enabled embedding of n-gram statistics. In: 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings. Paper presented at The International Joint Conference on Neural Networks (IJCNN 2021), virtual, July 18-22, 2021. IEEE. [BibTeX]
Rosato, A. , Panella, M. , Osipov, E. & Kleyko, D. (2021). On effects of compression with hyperdimensional computing in distributed randomized neural networks. In: Ignacio Rojas; Gonzalo Joya; Andreu Català, Advances in Computational Intelligence 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part II. Paper presented at 16th International Work-Conference on Artificial Neural Networks (IWANN 2021), Online, June 16-18, 2021. (pp. 155-167). Springer. [BibTeX]
Jain, H. , Agarwal, A. , Shridhar, K. & Kleyko, D. (2020). End to end binarized neural networks for text classification. Paper presented at Workshop on Simple and Efficient Natural Language Processing, SustaiNLP 2020, Online, November 20, 2020. (pp. 29-34). ACL. [BibTeX]
Kleyko, D. , Osipov, E. , De Silva, D. , Wiklund, U. , Vyatkin, V. & Alahakoon, D. (2019). Distributed representation of n-gram statistics for boosting self-organizing maps with hyperdimensional computing. In: Nikolaj Bjørner; Irina Virbitskaite; Andrei Voronkov, Perspectives of system informatics 12th International Andrei P. Ershov Informatics Conference, PSI 2019, Novosibirsk, Russia, July 2–5, 2019, Revised Selected Papers. Paper presented at 12th International Andrei P. Ershov Informatics Conference, (PSI 2019), Novosibirsk, Russia, July 2–5, 2019. (pp. 64-79). Cham: Springer. [BibTeX]
Kleyko, D. , Osipov, E. , De Silva, D. , Wiklund, U. & Alahakoon, D. (2019). Integer Self-Organizing Maps for Digital Hardware. In: 2019 International Joint Conference on Neural Networks (IJCNN). Paper presented at International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 14-19, 2019. IEEE. [BibTeX]
Karvonen, N. , Nilsson, J. , Kleyko, D. & Jimenez, L. L. (2019). Low-Power classification using FPGA: An approach based on cellular automata, neural networks, and hyperdimensional computing. In: M. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem (Jim) Seliya, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). Paper presented at 18th IEEE International Conference On Machine Learning And Applications (ICMLA 2019), Boca Raton, Florida, United States, December 16-19, 2019. (pp. 370-375). IEEE. [BibTeX]
Bandaragoda, T. , De Silva, D. , Kleyko, D. , Osipov, E. , Wiklund, U. & Alahakoon, D. (2019). Trajectory clustering of road traffic in urban environments using incremental machine learning in combination with hyperdimensional computing. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). Paper presented at 22nd Intelligent Transportation Systems Conference (ITSC2019), Auckland, New Zealand, October 27-30, 2019. (pp. 1664-1670). IEEE. [BibTeX]
Karvonen, N. & Kleyko, D. (2018). A domain knowledge-based solution for human activity recognition: The UJA Dataset Analysis. In: José Bravo; Oresti Baños, Proceedings, 2018, UCAmI 2018. Paper presented at 12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, December 4-7, 2018. (pp. 1-8). MDPI. [BibTeX]
Abdukalikova, A. , Kleyko, D. , Osipov, E. & Wiklund, U. (2018). Detection of atrial fibrillation from short ECGs: Minimalistic complexity analysis for feature-based classifiers. In: Christine Pickett; Cristiana Corsi; Pablo Laguna; Rob MacLeod, Computing in Cardiology 2018 Proceedings. Paper presented at 45th Computing in Cardiology (CinC 2018), Maastricht, The Netherlands, September 23-26, 2018. IEEE. [BibTeX]
Kleyko, D. & Osipov, E. (2018). No two brains are alike: cloning a hyperdimensional associative memory using cellular automata computations. In: Alexei V. Samsonovich; Valentin V. Klimov, Biologically Inspired Cognitive Architectures (BICA) for Young Scientists Proceedings of the First International Early Research Career Enhancement School on BICA and Cybersecurity (FIERCES 2017). Paper presented at 1st International Early Research Career Enhancement School on BICA and Cybersecurity (FIERCES 2017), Moscow, Russia, August 1-6, 2017. (pp. 91-100). Cham: Springer. [BibTeX]
Kleyko, D. , Khan, S. , Osipov, E. & Yong, S. (2017). Modality classification of medical images with distributed representations based on cellular automata reservoir computing. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Proceedings. Paper presented at 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, April 18-21, 2017. (pp. 1053-1056). IEEE. [BibTeX]
Kleyko, D. , Osipov, E. & Rachkovskij, D. A. (2016). Modification of Holographic Graph Neuron using Sparse Distributed Representations. In: Procedia Computer Science. Paper presented at 7th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA 2016), New York City, New York, United States, July 16-19, 2016. (pp. 39-45). Elsevier. [BibTeX]
Kleyko, D. , Osipov, E. & Gayler, R. W. (2016). Recognizing Permuted Words with Vector Symbolic Architectures: A Cambridge Test for Machines. Paper presented at 7th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA 2016), New York City, USA, July 16-19, 2016. (pp. 169-175). Elsevier. [BibTeX]
Wedekind, D. , Kleyko, D. , Osipov, E. , Malberg, H. , Zaunseder, S. & Wiklund, U. (2016). Sparse coding of cardiac signals for automated component selection after blind source separation. Paper presented at 43rd Computing in Cardiology Conference (CinC 2016), Vancouver, Canada, September 11-14, 2016. (pp. 785-788). Computing in Cardiology. [BibTeX]
Kleyko, D. , Hostettler, R. , Lyamin, N. , Birk, W. , Wiklund, U. & Osipov, E. (2016). Vehicle classification using road side sensors and feature-free data smashing approach. In: 2016 Ieee 19Th International Conference On Intelligent Transportation Systems (Itsc). Paper presented at 19th IEEE International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, BRAZIL, November 1-4, 2016. (pp. 1988-1993). IEEE. [BibTeX]
Kleyko, D. , Hostettler, R. , Birk, W. & Osipov, E. (2015). Comparison of machine learning techniques for vehicle classification using road side sensors. In: 2015 IEEE 18th International Conference on Intelligent Transportation Systems Proceedings. Paper presented at 18th IEEE International Conference on Intelligent Transportation Systems, Las Palmas, Spain, September 15-18, 2015. (pp. 572-577). IEEE. [BibTeX]
Kleyko, D. , Osipov, E. , Papakonstantinou, N. , Vyatkin, V. & Mousavi, A. (2015). Fault detection in the hyperspace: towards intelligent automation systems. In: 2015 IEEE 13th International Conference on Industrial Informatics (INDIN) Proceedings. Paper presented at 13th International Conference on Industrial Informatics, (INDIN 2015), Cambridge, United Kingdom, July 22-24, 2015. (pp. 1219-1224). IEEE. [BibTeX]
Kleyko, D. , Osipov, E. , Björk, M. , Toresson, H. & Öberg, A. (2015). Fly-The-Bee: A Game Imitating Concept Learning in Bees. Paper presented at 67th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA 2015), Lyon, France, November 6-8, 2015. (pp. 25-30). Elsevier. [BibTeX]
Kleyko, D. & Osipov, E. (2014). Brain-like classifier of temporal patterns. In: 2014 International Conference on Computer and Information Sciences (ICCOINS), 3-5 June 2014 Proceedings. Paper presented at International Conference on Computer and Information Sciences, Kuala Lampur, Malaysia, June 3-5, 2014. (pp. 1-6). Piscataway, NJ: IEEE Communications Society. [BibTeX]
Kleyko, D. , Lyamin, N. & Osipov, E. (2014). Modified algorithm of dynamic frequency hopping (DFH) in the IEEE 802.22 standard. In: Magnus Jonsson; Alexey Vinel; Boris Bellalta; Evgeny Belyaev, Multiple access communications 7th International Workshop, MACOM 2014, Halmstad, Sweden, August 27-28, 2014, Proceedings. Paper presented at 7th International Workshop on Multiple Access Communications (MACOM 2014), Halmstad, Sweden, August 27-28, 2014. (pp. 75-83). Springer. [BibTeX]
Kleyko, D. & Osipov, E. (2014). On bidirectional transitions between localist and distributed representations: The case of common substrings search using Vector Symbolic Architecture. Paper presented at 5th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA 2014), MIT Campus, Cambridge, MA, USA, November 7-9, 2014. (pp. 104-113). Elsevier. [BibTeX]
Kleyko, D. , Osipov, E. , Patil, S. , Vyatkin, V. & Pang, Z. (2014). On methodology of implementing distributed function block applications using TinyOS WSN nodes. In: Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA). Paper presented at 19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2014), Barcelona, Spain, September 16-19, 2014. IEEE. [BibTeX]
Kleyko, D. , Lyamin, N. , Osipov, E. & Riliskis, L. (2012). Dependable MAC layer architecture based on holographic data representation using hyper-dimensional binary spatter codes. In: Boris Bellalta; Alexey Vinel; Magnus Jonsson; Jaume Barcelo; Roman Maslennikov; Periklis Chatzimisios; David Malone, Multiple access communications 5th International Workshop, MACOM 2012, Maynooth, Ireland, November 19-20, 2012. Proceedings. Paper presented at 5th International Workshop on Multiple Access Communications (MACOM 2012), Maynooth, Ireland, November 19-20, 2012. (pp. 134-145). Springer. [BibTeX]

Doctoral theses, comprehensive summaries

Kleyko, D. (2018). Vector symbolic architectures and their applications: Computing with random vectors in a hyperdimensional space. (Doctoral dissertation). (Comprehensive summary) Luleå: Luleå University of Technology. [BibTeX]

Doctoral theses, monographs

Kleyko, D. (2018). Vector symbolic architectures and their applications: Computing with random vectors in a hyperdimensional space. (Doctoral dissertation). Luleå: Luleå tekniska universitet. [BibTeX]

Licentiate theses, comprehensive summaries

Kleyko, D. (2016). Pattern recognition with vector symbolic architectures. Lic. (Comprehensive summary) Luleå University of Technology. [BibTeX]

Manuscripts

Kymn, C. J. , Mazelet, S. , Thomas, A. , Kleyko, D. , Frady, E. P. , Sommer, F. T. & Olshausen, B. A. Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps. [BibTeX]
Kymn, C. J. , Kleyko, D. , Frady, E. P. , Bybee, C. , Kanerva, P. , Sommer, F. T. & Olshausen, B. A. Computing with Residue Numbers in High-Dimensional Representation. [BibTeX]

Reports

Osipov, E. , Riliskis, L. , Kleyko, D. & Lyamin, N. (2012). Packet-less medium access approach for dependable wireless event passing in highly noisy environments. Luleå: Luleå tekniska universitet (Teknisk rapport ). [BibTeX]