This page in English

Denis Kleyko

Befattning: Biträdande lektor Organisation: Institutionen för naturvetenskap och teknik

E-post: ZGVuaXMua2xleWtvO29ydS5zZQ==

Telefon: Telefonnummer saknas

Rum: T2227

Forskningsämne

Datavetenskap

Forskningsmiljöer

Centrum för tillämpade autonoma sensorsystem (AASS)

Om Denis Kleyko

Denis Kleyko är docent i datavetenskap vid Örebro universitet. Han disputerade i datavetenskap 2018 vid Luleå tekniska universitet. Innan han började vid Örebro universitet genomförde han tre postdoktorala vistelser vid olika institutioner. Den första vistelsen ägde rum vid Institutionen för system- och rymdteknik vid Luleå tekniska universitet (2018–2019). Den andra var vid Redwood Center for Theoretical Neuroscience vid University of California, Berkeley (2020–2022), och den tredje vid Intelligenta system enhet vid RISE Research Institutes of Sweden (2022–2023). De två senare vistelserna ingick i Marie Skłodowska-Curie Global Individual Fellowship, som Denis tilldelades 2019.

Denis fascination för forskning ligger i att undersöka beräkningar med slumpmässighet – hur komplex funktionalitet kan uppstå i till synes enkla algoritmer som använder någon form av slumpmässighet. Hans huvudsakliga intresse är ramverket för vektorsymboliska arkitekturer, även känt som hyperdimensionell beräkning, som utnyttjar slumpmässighet för kunskapsrepresentation, beräkning, inlärning och resonemang. Han undersöker också hur detta ramverk kan kopplas till framväxande energieffektiv hårdvara och hur det kan möjliggöra nya arkitekturer för artificiella neurala nätverk. Hans bredare forskningsintressen omfattar även informationsbearbetningsmetoder inspirerade av hjärnan och fysiken, såsom reservoir computing, associativa minnen, prototypbaserad inlärning, cellulära automater, sparse coding, kernel baserade metoder, Ising-maskiner, sketching-tekniker, och självorganiserande kartor.

Som en del av sitt samhällsengagemang är Denis webbansvarig och författare till en webbplats dedikerad till hyperdimensionell beräkning – www.hd-computing.com. Han är även medarrangör av en regelbunden webbinarieserie inom ämnet: “Online Speakers’ Corner on Vector Symbolic Architectures and Hyperdimensional Computing.”

Denis är Senior Member i IEEE, medlem i European Laboratory for Learning and Intelligent Systems (ELLIS Society) och medlem i Marie Curie Alumni Association.

Forskargrupper
Publikationer

Forskargrupper

Maskin Perception och Interaktion

Publikationer

Artiklar i tidskrifter

Kymn, C. J. , Kleyko, D. , Frady, E. P. , Bybee, C. , Kanerva, P. , Sommer, F. T. & Olshausen, B. A. (2025). Computing With Residue Numbers in High-Dimensional Representation. Neural Computation, 37 (1), 1-37. [BibTeX]
Kleyko, D. , Kymn, C. J. , Thomas, A. , Olshausen, B. A. , Sommer, F. T. & Frady, E. P. (2025). Principled neuromorphic reservoir computing. Nature Communications, 16 (1). [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]

Doktorsavhandlingar

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

Doktorsavhandlingar, sammanläggningar

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

Konferensbidrag

Kymn, C. J. , Mazelet, S. , Thomas, A. , Kleyko, D. , Frady, E. P. , Sommer, F. T. & Olshausen, B. A. (2024). Binding in Hippocampal-entorhinal Circuits Enables Compositionality in Cognitive Maps. I: Advances in Neural Information Processing Systems 38 (NeurIPS 2024) Proceedings of the international conference "Neural Information Processing Systems 2024". Konferensbidrag vid 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, Canada, December 10-15, 2024. Curran Associates, Inc., Neural Information Processing Systems Foundation Inc. [BibTeX]
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. I: 2024 Neuro Inspired Computational Elements Conference (NICE). Konferensbidrag vid Neuro Inspired Computational Elements Conference (NICE), La Jolla, CA, USA, April 23-26, 2024. IEEE. [BibTeX]
Kleyko, D. & Rachkovskij, D. A. (2024). On Design Choices in Similarity-Preserving Sparse Randomized Embeddings. I: 2024 International Joint Conference on Neural Networks (IJCNN). Konferensbidrag vid The International Joint Conference on Neural Networks (IJCNN 2024), Yokohama, Japan, June 30 - July 5, 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). I: NICE '22 Proceedings of the 2022 Annual Neuro-Inspired Computational Elements Conference. Konferensbidrag vid Annual Neuro-Inspired Computational Elements Conference, Nice, France, March 28 - April, 1, 2022. (ss. 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. I: 2022 International Joint Conference on Neural Networks (IJCNN) Proceedings. Konferensbidrag vid 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. I: NICE '22 Proceedings of the 2022 Annual Neuro-Inspired Computational Elements Conference. Konferensbidrag vid 2022 Annual Neuro-Inspired Computational Elements Conference, Nice, France, March 28 - April 1, 2022. (ss. 73-80). Association for Computing Machinery. [BibTeX]
Rachkovskij, D. A. & Kleyko, D. (2022). Recursive Binding for Similarity-Preserving Hypervector Representations of Sequences. I: 2022 International Joint Conference on Neural Networks (IJCNN) Proceedings. Konferensbidrag vid 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. I: 2021 International Joint Conference on Neural Networks (IJCNN). Konferensbidrag vid International Joint Conference on Neural Networks (IJCNN 2021), Virtual, Shenzhen, July 18-22, 2021. (ss. 1-9). IEEE. [BibTeX]
Rosato, A. , Panella, M. & Kleyko, D. (2021). Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks. I: 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings. Konferensbidrag vid 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. I: 2021 International Joint Conference on Neural Networks (IJCNN) Proceedings. Konferensbidrag vid 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. I: 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. Konferensbidrag vid 16th International Work-Conference on Artificial Neural Networks (IWANN 2021), Online, June 16-18, 2021. (ss. 155-167). Springer. [BibTeX]
Jain, H. , Agarwal, A. , Shridhar, K. & Kleyko, D. (2020). End to end binarized neural networks for text classification. Konferensbidrag vid Workshop on Simple and Efficient Natural Language Processing, SustaiNLP 2020, Online, November 20, 2020. (ss. 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. I: 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. Konferensbidrag vid 12th International Andrei P. Ershov Informatics Conference, (PSI 2019), Novosibirsk, Russia, July 2–5, 2019. (ss. 64-79). Cham: Springer. [BibTeX]
Kleyko, D. , Osipov, E. , De Silva, D. , Wiklund, U. & Alahakoon, D. (2019). Integer Self-Organizing Maps for Digital Hardware. I: 2019 International Joint Conference on Neural Networks (IJCNN). Konferensbidrag vid 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. I: M. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem (Jim) Seliya, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). Konferensbidrag vid 18th IEEE International Conference On Machine Learning And Applications (ICMLA 2019), Boca Raton, Florida, United States, December 16-19, 2019. (ss. 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. I: 2019 IEEE Intelligent Transportation Systems Conference (ITSC). Konferensbidrag vid 22nd Intelligent Transportation Systems Conference (ITSC2019), Auckland, New Zealand, October 27-30, 2019. (ss. 1664-1670). IEEE. [BibTeX]
Karvonen, N. & Kleyko, D. (2018). A domain knowledge-based solution for human activity recognition: The UJA Dataset Analysis. I: José Bravo; Oresti Baños, Proceedings, 2018, UCAmI 2018. Konferensbidrag vid 12th International Conference on Ubiquitous Computing and Ambient ‪Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, December 4-7, 2018. (ss. 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. I: Christine Pickett; Cristiana Corsi; Pablo Laguna; Rob MacLeod, Computing in Cardiology 2018 Proceedings. Konferensbidrag vid 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. I: 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). Konferensbidrag vid 1st International Early Research Career Enhancement School on BICA and Cybersecurity (FIERCES 2017), Moscow, Russia, August 1-6, 2017. (ss. 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. I: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Proceedings. Konferensbidrag vid 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, April 18-21, 2017. (ss. 1053-1056). IEEE. [BibTeX]
Kleyko, D. , Osipov, E. & Rachkovskij, D. A. (2016). Modification of Holographic Graph Neuron using Sparse Distributed Representations. I: Procedia Computer Science. Konferensbidrag vid 7th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA 2016), New York City, New York, United States, July 16-19, 2016. (ss. 39-45). Elsevier. [BibTeX]
Kleyko, D. , Osipov, E. & Gayler, R. W. (2016). Recognizing Permuted Words with Vector Symbolic Architectures: A Cambridge Test for Machines. Konferensbidrag vid 7th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA 2016), New York City, USA, July 16-19, 2016. (ss. 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. Konferensbidrag vid 43rd Computing in Cardiology Conference (CinC 2016), Vancouver, Canada, September 11-14, 2016. (ss. 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. I: 2016 Ieee 19Th International Conference On Intelligent Transportation Systems (Itsc). Konferensbidrag vid 19th IEEE International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, BRAZIL, November 1-4, 2016. (ss. 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. I: 2015 IEEE 18th International Conference on Intelligent Transportation Systems Proceedings. Konferensbidrag vid 18th IEEE International Conference on Intelligent Transportation Systems, Las Palmas, Spain, September 15-18, 2015. (ss. 572-577). IEEE. [BibTeX]
Kleyko, D. , Osipov, E. , Papakonstantinou, N. , Vyatkin, V. & Mousavi, A. (2015). Fault detection in the hyperspace: towards intelligent automation systems. I: 2015 IEEE 13th International Conference on Industrial Informatics (INDIN) Proceedings. Konferensbidrag vid 13th International Conference on Industrial Informatics, (INDIN 2015), Cambridge, United Kingdom, July 22-24, 2015. (ss. 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. Konferensbidrag vid 67th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA 2015), Lyon, France, November 6-8, 2015. (ss. 25-30). Elsevier. [BibTeX]
Kleyko, D. & Osipov, E. (2014). Brain-like classifier of temporal patterns. I: 2014 International Conference on Computer and Information Sciences (ICCOINS), 3-5 June 2014 Proceedings. Konferensbidrag vid International Conference on Computer and Information Sciences, Kuala Lampur, Malaysia, June 3-5, 2014. (ss. 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. I: Magnus Jonsson; Alexey Vinel; Boris Bellalta; Evgeny Belyaev, Multiple access communications 7th International Workshop, MACOM 2014, Halmstad, Sweden, August 27-28, 2014, Proceedings. Konferensbidrag vid 7th International Workshop on Multiple Access Communications (MACOM 2014), Halmstad, Sweden, August 27-28, 2014. (ss. 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. Konferensbidrag vid 5th Annual International Conference on Biologically Inspired Cognitive Architectures (BICA 2014), MIT Campus, Cambridge, MA, USA, November 7-9, 2014. (ss. 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. I: Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA). Konferensbidrag vid 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. I: 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. Konferensbidrag vid 5th International Workshop on Multiple Access Communications (MACOM 2012), Maynooth, Ireland, November 19-20, 2012. (ss. 134-145). Springer. [BibTeX]

Licentiatavhandlingar, sammanläggningar

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

Manuskript

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]

Rapporter

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]