Publications

Here is a list of our recent publications.

Submitted publications and preprints

• Y. Liu, L. Guo, V. A. Makarov, Y. Huang, A. N. Gorban, E. Mirkes, and I. Y. Tyukin (2023). Agile gesture recognition for capacitive sensing devices: adapting on-the-job, arXiv 2305.07624.
• O. J. Sutton, Q. Zhou, I. Y Tyukin, A. N. Gorban, A. Bastounis, and D. J. Higham (2023). How adversarial attacks can disrupt seemingly stable accurate classifiers, arXiv 2309.03665.
• O. J. Sutton, A. N. Gorban, and I. Y. Tyukin (2022). Towards a mathematical understanding of learning from few examples with nonlinear feature maps, arXiv 2211.03607.

Conference Proceedings

• Q. Zhou, O. J. Sutton, Y. Zhang, A. N. Gorban, V. A. Makarov, and I. Y. Tyukin (2023). Neuromorphic tuning of feature spaces to overcome the challenge of low-sample high-dimensional data, International Joint Conference On Neural Networks (IJCNN) 2023.
• I. Y. Tyukin, O. J. Sutton, and A. N. Gorban (2023). Learning from few examples with nonlinear feature maps, Science and Information Conference (2023) vol 711, pp 210-225, Springer, Cham. Winner of best paper award.
• O. J. Sutton, A. N. Gorban, and I. Y. Tyukin (2023). A geometric view on the role of nonlinear feature maps in few-shot learning, Geometric Science of Information (GSI) 2023 vol 14071, pp 560-568, Springer, Cham.
• O. J. Sutton, Q. Zhou, A. N. Gorban, and I. Y. Tyukin (2023). Relative intrinsic dimensionality is intrinsic to learning, International Conference on Artificial Neural Networks (ICANN) 2023 vol 14254, pp 516-529, Springer, Cham.
• A. Bastounis, A. N. Gorban, A. C. Hansen, D. J. Higham, D. Prokhorov, O. J. Sutton, I. Y. Tyukin, and Q. Zhou (2023). The boundaries of verifiable accuracy, robustness, and generalisation in deep learning, International Conference on Artificial Neural Networks (ICANN) 2023 vol 14254, pp 530-541, Springer, Cham.
• Q. Zhou, A. N. Gorban, E. M. Mirkes, J. Bac, A. Zinovyev, and I. Y. Tyukin (2022). Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation, International Joint Conference On Neural Networks (IJCNN) 2022.
• G. D. Leete, A. N. Gorban, and I. Y. Tyukin (2022). Fast situation-based correction of AI systems, 4th International Conference on Industrial Artificial Intelligence (ICIAI).
• I. Y. Tyukin, A. N. Gorban, M. H. Alkhudaydi, and Q. Zhou (2021). Demystification of Few-shot and One-shot Learning, 2021 International Joint Conference On Neural Networks (IJCNN).
• U. C. Turker, R. M. Hierons, M. R. Mousavi, and I. Y. Tyukin (2021). Efficient state synchronisation in model-based testing through reinforcement learning, 2021 36th IEEE/ACM International Conference On Automated Software Engineering ASE 2021.
• S. Wang, G. McCann, and I. Y. Tyukin (2020). Myocardial Infarction Detection and Quantification Based on a Convolution Neural Network with Online Error Correction Capabilities, 2020 International Joint Conference On Neural Networks (IJCNN).
• I. Y. Tyukin, D. J. Higham, and A. N. Gorban (2020). On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems, 2020 International Joint Conference On Neural Networks (IJCNN).
• R. Fenwick, H. Boecsh, and I. Y. Tyukin (2020). Neural Networks for the Retrieval of Methane from the Sentinel-5 Precursor Satellite, 2020 International Joint Conference On Neural Networks (IJCNN).
• I. Y. Tyukin, A. N. Gorban, B. Grechuk, and S. Green (2019). Kernel Stochastic Separation Theorems and Separability Characterizations of Kernel Classifiers, 2019 International Joint Conference On Neural Networks (IJCNN).
• I. Y. Tyukin, A. N. Gorban, A. McEwan, and S. Meshkinfamfard (2019). Bringing the Blessing of Dimensionality to the Edge, 2019 1st International Conference On Industrial Artificial Intelligence (IAI 2019).
• I. Y. Tyukin, A. N. Gorban, D. Prokhorov V, and S. Green (2018). Efficiency of Shallow Cascades for Improving Deep Learning AI Systems, 2018 International Joint Conference On Neural Networks (IJCNN).
• S. Meshkinfamfard, A. N. Gorban, and I. Y. Tyukin (2018). Tackling Rare False-Positives in Face Recognition: a Case Study, IEEE 20th International Conference On High Performance Computing And Communications / IEEE 16th International Conference On Smart City / IEEE 4th International Conference On Data Science And Systems (HPCC/Smartcity/DSS).
• A. N. Gorban, I. Y. Tyukin, and H. Nijmeijer (2014). Further Results on Lyapunov-Like Conditions of Forward Invariance and Boundedness for a Class of Unstable Systems, 2014 IEEE 53Rd Annual Conference On Decision And Control (CDC).
• I. Y. Tyukin, and D. V. Prokhorov (2009). Feasibility of random basis function approximators for modeling and control, 2009 IEEE Control Applications Cca \& Intelligent Control (Isic), Vols 1-3.

Peer reviewed journal publications

• M. R. Mousavi, A. Cavalcanti, M. Fisher, L. Dennis, R. Hierons, B. Kaddouh, E. L. Law, R. Richardson, J. O. Ringer, I. Y. Tyukin, and J. Woodcock (2023). Trustworthy Autonomous Systems Through Verifiability, Computer 56(2), pages 40-47.
• S. Wang, A. M. S. E. K. Abdelaty, K. Parke, J. R. Arnold, G. P. McCann, and I. Y. Tyukin (2023). MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images, Entropy 25(3).
• A. Markfort, A. Baranov, T. M. Conneely, A. Duran, J. Milnes, A. Mudrov, J. Lapington, and I. Y. Tyukin (2023). Utilising machine learning algorithms for high speed data processing of a single photon counting 256 channel PMT with a timing resolution of 60 ps, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 1047.
• D. Lukashevich, G. V. Ovchinnikov, I. Y. Tyukin, S. A. Matveev, and N. V. Brilliantov (2022). Data-Driven Approach for Modeling Coagulation Kinetics, Computational Mathematics and Modeling 33(3), 310-318.
• A. Markfort, A. Baranov, T. M. Conneely, A. Duran, J. Lapington, J. Milnes, W. Moore, A. Mudrov, and I. Y. Tyukin (2022). Investigating machine learning solutions for a 256 channel TCSPC camera with sub-70 ps single photon timing per channel at data rates >10 Gbps, Journal of Instrumentation Volume 17, Issue 7, C07023.
• D. van Helden, E. Mirkes, I. Y. Tyukin, and P. Allison (2022). The Arch-I-Scan Project: artificial intelligence and 3D simulation for developing new approaches to Roman foodways, Journal of Computer Applications in Archaeology 5(1), 78-95.
• J. Barker, X. Li, S. Khavandi, D. Koeckerling, A. Mavilakandy, C. Pepper, V. Bountziouka, L. Chen, A. Kotb, I. Antoun, J. Mansir, K. Smith-Byrne, F. S. Schlindwein, H. Dhutia, I. Y. Tyukin, W. B. Nicolson, and G. Andre Ng (2022). Machine learning in sudden cardiac death risk prediction: a systematic review, Europace 24(11), pages 1777-1787.
• B. Grechuk, A. N. Gorban, and I. Y. Tyukin (2021). General stochastic separation theorems with optimal bounds, Neural Networks 138, pages 33-56.
• I. Y. Tyukin, A. N. Gorban, A. A. McEwan, S. Meshkinfamfard, and L. Tang (2021). Blessing of dimensionality at the edge and geometry of few-shot learning, Information Sciences 564, pages 124-143.
• S. Wang, M. E. Celebi, Y. Zhang, X. Yu, S. Lu, X. Yao, Q. Zhou, M. Miguel, Y. Tian, J. M. Gorriz, and I. Y. Tyukin (2021). Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects, Information Fusion 76, pages 376-421.
• A. N. Gorban, B. Grechuk, E. M. Mirkes, S. Stasenko V, and I. Y. Tyukin (2021). High-Dimensional Separability for One- and Few-Shot Learning, Entropy 23(8).
• S. J. N. Jareno, D. P. van Helden, E. M. Mirkes, I. Y. Tyukin, and P. M. Allison (2021). Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery, Entropy 23(9).
• J. Bac, E. M. Mirkes, A. N. Gorban, I. Y. Tyukin, and A. Zinovyev (2021). Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation, Entropy 23(10).
• A. N. Gorban, V. A. Makarov, and I. Y. Tyukin (2020). High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality, Entropy 22(1).
• A. N. Gorban, E. M. Mirkes, and I. Y. Tyukin (2020). How Deep Should be the Depth of Convolutional Neural Networks: a Backyard Dog Case Study, Cognitive Computation 12(2, SI), pages 388-397.
• C. Calvo Tapia, I. Y. Tyukin, and V. A. Makarov (2020). Universal principles justify the existence of concept cells, Scientific Reports 10(1).
• N. V. Brilliantov, H. Abutuqayqah, I. Y. Tyukin, and S. A. Matveev (2020). Swirlonic state of active matter, Scientific Reports 10(1).
• E. Wilson, T. M. Conneely, A. Mudrov, and I. Y. Tyukin (2019). Implementation of the Prony Method for Signal Deconvolution, IFAC Papersonline 52(29), pages 269-273.
• A. N. Gorban, R. Burton, I. Romanenko, and I. Y. Tyukin (2019). One-trial correction of legacy AI systems and stochastic separation theorems, Information Sciences 484, pages 237-254.
• I. Y. Tyukin, A. N. Gorban, S. Green, and D. Prokhorov (2019). Fast construction of correcting ensembles for legacy Artificial Intelligence systems: Algorithms and a case study, Information Sciences 485, pages 230-247.
• I. Y. Tyukin, D. Iudin, F. Iudin, T. Tyukina, V. Kazantsev, I. Mukhina, and A. N. Gorban (2019). Simple model of complex dynamics of activity patterns in developing networks of neuronal cultures, PLoS ONE 14(6).
• A. N. Gorban, V. A. Makarov, and I. Y. Tyukin (2019). Symphony of high-dimensional brain Reply to comments on ''The unreasonable effectiveness of small neural ensembles in high-dimensional brain'', Physics Of Life Reviews 29, pages 115-119.
• A. N. Gorban, V. A. Makarov, and I. Y. Tyukin (2019). The unreasonable effectiveness of small neural ensembles in high-dimensional brain, Physics Of Life Reviews 29, pages 55-88.
• I. Y. Tyukin, A. N. Gorban, C. Calvo, J. Makarova, and V. A. Makarov (2019). High-Dimensional Brain: A Tool for Encoding and Rapid Learning of Memories by Single Neurons, Bulletin Of Mathematical Biology 81(11, SI), pages 4856-4888.
• C. Calvo Tapia, I. Y. Tyukin, and V. A. Makarov (2018). Fast social-like learning of complex behaviors based on motor motifs, Physical Review E 97(5).
• I. Y. Tyukin, A. N. Gorban, K. I. Sofeykov, and I. Romanenko (2018). Knowledge Transfer Between Artificial Intelligence Systems, Frontiers In Neurorobotics 12.
• P. N. Suganthan, S. Mitra, and I. Y. Tyukin (2018). Preface special issue on non-iterative approaches in learning, Applied Soft Computing 70, pages 947-950.
• N. Jarman, E. Steur, C. Trengove, I. Y. Tyukin, and C. van Leeuwen (2017). Self-organisation of small-world networks by adaptive rewiring in response to graph diffusion, Scientific Reports 7.
• D. I. Iudin, I. Y. Tyukin, A. N. Gorban, F. D. Iudin, V. B. Kazantsev, I. V. Muhina, and T. T. Tyukina (2016). Simple model of complex bursting dynamics in developing networks of neuronal cultures, IFAC Papersonline 49(14), pages 68-73.
• A. N. Gorban, I. Y. Tyukin, and I. Romanenko (2016). The Blessing of Dimensionality: Separation Theorems in the Thermodynamic Limit, IFAC Papersonline 49(24), pages 64-69.
• A. N. Gorban, I. Y. Tyukin, D. V. Prokhorov, and K. I. Sofeikov (2016). Approximation with random bases: Pro et Contra, Information Sciences 364, pages 129-145.
• G. Fridman, J. Levesley, I. Y. Tyukin, and D. Wunsch (2015). Preface, Mathematical Modelling Of Natural Phenomena 10(3), pages 1-5.
• I. Prokin, I. Y. Tyukin, and V. Kazantsev (2015). Phase Selective Oscillations in Two Noise Driven Synaptically Coupled Spiking Neurons, International Journal Of Bifurcation And Chaos 25(7).
• N. Jarman, C. Trengove, E. Steur, I. Y. Tyukin, and C. van Leeuwen (2014). Spatially constrained adaptive rewiring in cortical networks creates spatially modular small world architectures, Cognitive Neurodynamics 8(6), pages 479-497.
• I. Y. Tyukin, E. Steur, H. Nijmeijer, and C. van Leeuwen (2013). Adaptive observers and parameter estimation for a class of systems nonlinear in the parameters, Automatica 49(8), pages 2409-2423.
• P. Jurica, S. Gepshtein, I. Y. Tyukin, and C. van Leeuwen (2013). Sensory Optimization by Stochastic Tuning, Psychological Review 120(4), pages 798-816.
• V. Kazantsev, and I. Y. Tyukin (2012). Adaptive and Phase Selective Spike Timing Dependent Plasticity in Synaptically Coupled Neuronal Oscillators, PLoS ONE 7(3).
• I. Y. Tyukin, E. Steur, H. Nijmeijer, D. Fairhurst, I. Song, A. Semyanov, and C. Van Leeuwen (2010). State And Parameter Estimation For Canonic Models Of Neural Oscillators, International Journal Of Neural Systems 20(3), pages 193-207.
• I. Y. Tyukin, T. Tyukina, and C. van Leeuwen (2009). Invariant template matching in systems with spatiotemporal coding: A matter of instability, Neural Networks 22(4), pages 425-449.
• E. Steur, I. Y. Tyukin, and H. Nijmeijer (2009). Semi-passivity and synchronization of diffusively coupled neuronal oscillators, Physica D-Nonlinear Phenomena 238(21), pages 2119-2128.
• I. Y. Tyukin, E. Steur, H. Nijmeijer, and C. Van Leeuwen (2008). Nonuniform small-gain theorems for systems with unstable invariant sets, SIAM Journal On Control And Optimization 47(2), pages 849-882.
• I. Y. Tyukin, D. Prokhorov, and C. Van Leeuwen (2008). Adaptive classification of temporal signals in fixed-weight recurrent neural networks: An existence proof, Neural Computation 20(10), pages 2564-2596.