Oliver J. Sutton
Hivatkozott rá
Hivatkozott rá
Conforming and nonconforming virtual element methods for elliptic problems
A Cangiani, G Manzini, OJ Sutton
IMA Journal of Numerical Analysis 37 (3), 1317-1354, 2017
A posteriori error estimates for the virtual element method
A Cangiani, EH Georgoulis, T Pryer, OJ Sutton
Numerische mathematik 137, 857-893, 2017
Introduction to k nearest neighbour classification and condensed nearest neighbour data reduction
O Sutton
University lectures, University of Leicester, 2012
The virtual element method in 50 lines of MATLAB
OJ Sutton
Numerical Algorithms 75, 1141-1159, 2017
Virtual element methods for elliptic problems on polygonal meshes
A Cangiani, OJ Sutton, V Gyrya, G Manzini
Generalized Barycentric Coordinates in Computer Graphics and Computational …, 2017
Revealing new dynamical patterns in a reaction–diffusion model with cyclic competition via a novel computational framework
A Cangiani, EH Georgoulis, AY Morozov, OJ Sutton
Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2018
Adaptive non-hierarchical Galerkin methods for parabolic problems with application to moving mesh and virtual element methods
A Cangiani, EH Georgoulis, OJ Sutton
Mathematical Models and Methods in Applied Sciences 31 (04), 711-751, 2021
Long-time L(L2) a posteriori error estimates for fully discrete parabolic problems
OJ Sutton
IMA Journal of Numerical Analysis 40 (1), 498-529, 2020
Virtual element methods
OJ Sutton
University of Leicester, 2017
Efficient High-Order Space-Angle-Energy Polytopic Discontinuous Galerkin Finite Element Methods for Linear Boltzmann Transport
P Houston, ME Hubbard, TJ Radley, OJ Sutton, RSJ Widdowson
arXiv preprint arXiv:2304.09592, 2023
Residual-Based A Posteriori Error Estimates for -Discontinuous Galerkin Discretizations of the Biharmonic Problem
Z Dong, L Mascotto, OJ Sutton
SIAM Journal on Numerical Analysis 59 (3), 1273-1298, 2021
The Conforming Virtual Element Method for the convection-diffusion-reaction equation with variable coeffcients.
G Manzini, A Cangiani, O Sutton
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2014
Towards a mathematical understanding of learning from few examples with nonlinear feature maps
OJ Sutton, AN Gorban, IY Tyukin
arXiv preprint arXiv:2211.03607, 2022
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning
A Bastounis, AN Gorban, AC Hansen, DJ Higham, D Prokhorov, O Sutton, ...
International Conference on Artificial Neural Networks, 530-541, 2023
Learning from few examples with nonlinear feature maps
IY Tyukin, O Sutton, AN Gorban
Science and Information Conference, 210-225, 2023
Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees
IY Tyukin, T Tyukina, D van Helden, Z Zhang, EM Mirkes, OJ Sutton, ...
arXiv preprint arXiv:2402.00899, 2024
Relative Intrinsic Dimensionality Is Intrinsic to Learning
OJ Sutton, Q Zhou, AN Gorban, IY Tyukin
International Conference on Artificial Neural Networks, 516-529, 2023
How adversarial attacks can disrupt seemingly stable accurate classifiers
OJ Sutton, Q Zhou, IY Tyukin, AN Gorban, A Bastounis, DJ Higham
arXiv preprint arXiv:2309.03665, 2023
A Geometric View on the Role of Nonlinear Feature Maps in Few-Shot Learning
OJ Sutton, AN Gorban, IY Tyukin
International Conference on Geometric Science of Information, 560-568, 2023
Neuromorphic tuning of feature spaces to overcome the challenge of low-sample high-dimensional data
Q Zhou, OJ Sutton, YD Zhang, AN Gorban, VA Makarov, IY Tyukin
2023 International Joint Conference on Neural Networks (IJCNN), 1-8, 2023
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