Ivan Tyukin

Orcid: 0000-0002-7359-7966

According to our database1, Ivan Tyukin authored at least 72 papers between 2000 and 2024.

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Bibliography

2024
Knowledge-informed neuro-integrators for aggregation kinetics.
Commun. Nonlinear Sci. Numer. Simul., April, 2024

Accelerating Finite State Machine-Based Testing Using Reinforcement Learning.
IEEE Trans. Software Eng., March, 2024

Coping with AI errors with provable guarantees.
Inf. Sci., 2024

Stealth edits for provably fixing or attacking large language models.
CoRR, 2024

Agile gesture recognition for low-power applications: customisation for generalisation.
CoRR, 2024

Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees.
CoRR, 2024

2023
GeoAI in urban analytics.
Int. J. Geogr. Inf. Sci., December, 2023

MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images.
Entropy, March, 2023

Trustworthy Autonomous Systems Through Verifiability.
Computer, February, 2023

Relative intrinsic dimensionality is intrinsic to learning.
CoRR, 2023

The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning.
CoRR, 2023

How adversarial attacks can disrupt seemingly stable accurate classifiers.
CoRR, 2023

Neuromorphic tuning of feature spaces to overcome the challenge of low-sample high-dimensional data.
Proceedings of the International Joint Conference on Neural Networks, 2023

Agile gesture recognition for capacitive sensing devices: adapting on-the-job.
Proceedings of the International Joint Conference on Neural Networks, 2023

A Geometric View on the Role of Nonlinear Feature Maps in Few-Shot Learning.
Proceedings of the Geometric Science of Information - 6th International Conference, 2023

2022
Towards a mathematical understanding of learning from few examples with nonlinear feature maps.
CoRR, 2022

Learning from few examples with nonlinear feature maps.
CoRR, 2022

Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation.
Proceedings of the International Joint Conference on Neural Networks, 2022

2021
General stochastic separation theorems with optimal bounds.
Neural Networks, 2021

Blessing of dimensionality at the edge and geometry of few-shot learning.
Inf. Sci., 2021

Advances in Data Preprocessing for Biomedical Data Fusion: An Overview of the Methods, Challenges, and Prospects.
Inf. Fusion, 2021

Learning from Scarce Information: Using Synthetic Data to Classify Roman Fine Ware Pottery.
Entropy, 2021

High-Dimensional Separability for One- and Few-Shot Learning.
Entropy, 2021

Scikit-Dimension: A Python Package for Intrinsic Dimension Estimation.
Entropy, 2021

The Feasibility and Inevitability of Stealth Attacks.
CoRR, 2021

Efficient state synchronisation in model-based testing through reinforcement learning.
Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering, 2021

Demystification of Few-shot and One-shot Learning.
Proceedings of the International Joint Conference on Neural Networks, 2021

2020
High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality.
Entropy, 2020

How Deep Should be the Depth of Convolutional Neural Networks: a Backyard Dog Case Study.
Cogn. Comput., 2020

Myocardial Infarction Detection and Quantification Based on a Convolution Neural Network with Online Error Correction Capabilities.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Neural Networks for the Retrieval of Methane from the Sentinel-5 Precursor Satellite.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019
Fast construction of correcting ensembles for legacy Artificial Intelligence systems: Algorithms and a case study.
Inf. Sci., 2019

One-trial correction of legacy AI systems and stochastic separation theorems.
Inf. Sci., 2019

Blessing of dimensionality at the edge.
CoRR, 2019

Symphony of high-dimensional brain.
CoRR, 2019

Using Convolutional Neural Networks to Distinguish Different Sign Language Alphanumerics.
Proceedings of the Recent Advances in Big Data and Deep Learning, 2019

Kernel Stochastic Separation Theorems and Separability Characterizations of Kernel Classifiers.
Proceedings of the International Joint Conference on Neural Networks, 2019

2018
Correction of AI systems by linear discriminants: Probabilistic foundations.
Inf. Sci., 2018

Knowledge Transfer Between Artificial Intelligence Systems.
Frontiers Neurorobotics, 2018

The unreasonable effectiveness of small neural ensembles in high-dimensional brain.
CoRR, 2018

Augmented Artificial Intelligence: a Conceptual Framework.
CoRR, 2018

Blessing of dimensionality: mathematical foundations of the statistical physics of data.
CoRR, 2018

Preface special issue on non-iterative approaches in learning.
Appl. Soft Comput., 2018

Efficiency of Shallow Cascades for Improving Deep Learning AI Systems.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Tackling Rare False-Positives in Face Recognition: A Case Study.
Proceedings of the 20th IEEE International Conference on High Performance Computing and Communications; 16th IEEE International Conference on Smart City; 4th IEEE International Conference on Data Science and Systems, 2018

2017
Stochastic separation theorems.
Neural Networks, 2017

2016
Approximation with random bases: Pro et Contra.
Inf. Sci., 2016

The Blessing of Dimensionality: Separation Theorems in the Thermodynamic Limit.
CoRR, 2016

2015
Phase Selective Oscillations in Two Noise Driven Synaptically Coupled Spiking Neurons.
Int. J. Bifurc. Chaos, 2015

2014
Optimal measurement of visual motion across spatial and temporal scales.
CoRR, 2014

Learning optimization for decision tree classification of non-categorical data with information gain impurity criterion.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

Scene analysis assisting for AWB using binary decision trees and average image metrics.
Proceedings of the IEEE International Conference on Consumer Electronics, 2014

Further results on Lyapunov-like conditions of forward invariance and boundedness for a class of unstable systems.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

2013
Lyapunov-like Conditions of Forward Invariance and Boundedness for a Class of Unstable Systems.
SIAM J. Control. Optim., 2013

Adaptive observers and parameter estimation for a class of systems nonlinear in the parameters.
Autom., 2013

Explicit reduced-order integral formulations of state and parameter estimation problems for a class of nonlinear systems.
Proceedings of the 52nd IEEE Conference on Decision and Control, 2013

2010
State and Parameter Estimation for Canonic Models of Neural oscillators.
Int. J. Neural Syst., 2010

Supplement: The uncertainty principle of measurement in vision
CoRR, 2010

2009
Invariant template matching in systems with spatiotemporal coding: A matter of instability.
Neural Networks, 2009

Feasibility of random basis function approximators for modeling and control.
Proceedings of the IEEE International Conference on Control Applications, 2009

2008
Nonuniform Small-Gain Theorems for Systems with Unstable Invariant Sets.
SIAM J. Control. Optim., 2008

Adaptive Classification of Temporal Signals in Fixed-Weight Recurrent Neural Networks: An Existence Proof.
Neural Comput., 2008

Non-uniform small-gain theorems for systems with unstable invariant sets.
Proceedings of the 47th IEEE Conference on Decision and Control, 2008

2007
Adaptation and Parameter Estimation in Systems With Unstable Target Dynamics and Nonlinear Parametrization.
IEEE Trans. Autom. Control., 2007

Invariant template matching in systems with spatiotemporal coding: a vote for instability
CoRR, 2007

2005
A new method for adaptive brake control.
Proceedings of the American Control Conference, 2005

2003
Adaptive control with nonconvex parameterization.
IEEE Trans. Autom. Control., 2003

Parameter Estimation of Sigmoid Superpositions: Dynamical System Approach.
Neural Comput., 2003

Finite form realizations of adaptive control algorithms.
Proceedings of the 7th European Control Conference, 2003

2002
On a problem of time-varying learning rate influence on the adaptive system dynamics.
Proceedings of the 41st IEEE Conference on Decision and Control, 2002

2000
Adaptive control on manifolds with RBF neural networks.
Proceedings of the 39th IEEE Conference on Decision and Control, 2000


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