World's Best Scientists 2026 revealed!

Overview

Steve R. Gunn is affiliated with the University of Southampton in the United Kingdom. Their research spans primarily the fields of Computer Science and Engineering, with a notable focus on Artificial Intelligence and its related subfields. Within their body of work, they have contributed significantly to areas such as Sparse and Compressive Sensing Techniques, Stochastic Gradient Optimization Techniques, and Neural Networks and Applications.

Their research output includes papers published in various scientific venues, notably in IEEE Control Systems Letters, International Journal of Artificial Intelligence Tools, Proceedings of the Python in Science Conferences, and arXiv (Cornell University). These journals and conference proceedings cover topics ranging from control systems to embedded intelligence and computational resource optimization.

Key recent publications by Steve R. Gunn include:

  • Strengthened Circle and Popov Criteria for the Stability Analysis of Feedback Systems With ReLU Neural Networks (2023), published in IEEE Control Systems Letters
  • Sparse Deep Neural Network Optimization for Embedded Intelligence (2020), published in International Journal of Artificial Intelligence Tools
  • Computational Resource Optimisation in Feature Selection under Class Imbalance Conditions (2024), published in Proceedings of the Python in Science Conferences
  • A Variance Controlled Stochastic Method with Biased Estimation for Faster Non-convex Optimization (2021), published in arXiv (Cornell University)
  • Errata on "Strengthened Circle and Popov Criteria for the Stability Analysis of Feedback Systems With ReLU Neural Networks" (2023), published in IEEE Control Systems Letters

The scientist's frequent coauthors include Jia Bi, Carl R. Richardson, Matthew C. Turner, Amadi Gabriel Udu, and Andrea Lecchini-Visintini, indicating collaborative efforts across multiple research projects.

Steve R. Gunn's work covers a range of subfields including Artificial Intelligence, Computational Mechanics, Control and Systems Engineering, Computer Networks and Communications, and Computer Vision and Pattern Recognition. The emphasis on neural networks is underscored by topics such as Neural Networks Stability and Synchronization and Neural Networks and Reservoir Computing, alongside applications in advanced neural network methodologies.

The publication record shows a pattern of engagement with topics vital to the development of AI techniques, leveraging mathematical frameworks and optimization algorithms to improve embedded intelligence and resource-efficient computational methods.

Best Publications

  • Support Vector Machines for Classification and Regression

    S.R. Gunn

  • Feature extraction : foundations and applications

    I.M. Guyon;S.R. Gunn;M. Nikravesh;L. Zadeh

  • Result Analysis of the NIPS 2003 Feature Selection Challenge

    Isabelle Guyon;Steve Gunn;Asa Ben-Hur;Gideon Dror

  • Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)

    Isabelle Guyon;Steve Gunn;Masoud Nikravesh;Lotfi A. Zadeh

  • Positron Emission Tomography Compartmental Models

    Roger N. Gunn;Steve R. Gunn;Vincent J. Cunningham

  • Band Selection for Hyperspectral Image Classification Using Mutual Information

    Baofeng Guo;S.R. Gunn;R.I. Damper;J.D.B. Nelson

  • A robust snake implementation; a dual active contour

    S.R. Gunn;M.S. Nixon

  • Linear spectral mixture models and support vector machines for remote sensing

    M. Brown;H.G. Lewis;S.R. Gunn

  • Positron emission tomography compartmental models: a basis pursuit strategy for kinetic modeling.

    Roger N Gunn;Steve R Gunn;Federico E Turkheimer;John A D Aston;John A D Aston

  • Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification

    Baofeng Guo;S.R. Gunn;R.I. Damper;J.D.B. Nelson

  • Support vector machines for optimal classification and spectral unmixing

    Martin Brown;Steve R. Gunn;Hugh G. Lewis

  • A Probabilistic Framework for SVM Regression and Error Bar Estimation

    J. B. Gao;S. R. Gunn;C. J. Harris;M. Brown

  • Network Performance Assessment for Neurofuzzy Data Modelling

    Steve R. Gunn;Martin Brown;Kev M. Bossley

  • A fast separability-based feature-selection method for high-dimensional remotely sensed image classification

    Baofeng Guo;R. I. Damper;Steve R. Gunn;J. D. B. Nelson

  • Structural Modelling with Sparse Kernels

    S. R. Gunn;J. S. Kandola

  • On the discrete representation of the Laplacian of Gaussian

    Steve R. Gunn

  • Identifying feature relevance using a random forest

    Jeremy Rogers;Steve Gunn

  • Subspace, Latent Structure and Feature Selection

    Craig Saunders;Marko Grobelnik;Steve Gunn;John Shawe-Taylor

  • The relevance vector machine technique for channel equalization application

    S. Chen;S.R. Gunn;C.J. Harris

  • Decision feedback equaliser design using support vector machines

    S. Chen;S.R. Gunn;C.J. Harris

Frequent Co-Authors

Mark S. Nixon
Mark S. Nixon University of Southampton
Junbin Gao
Junbin Gao University of Sydney
John Shawe-Taylor
John Shawe-Taylor University College London
Roger N. Gunn
Roger N. Gunn Imperial College London
Bashir M. Al-Hashimi
Bashir M. Al-Hashimi King's College London
Vincent J. Cunningham
Vincent J. Cunningham University of Aberdeen
Marko Grobelnik
Marko Grobelnik Jožef Stefan Institute
Byron Blomquist
Byron Blomquist University of Colorado Boulder
Federico E. Turkheimer
Federico E. Turkheimer King's College London
Ian Sinclair
Ian Sinclair University of Southampton

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