How to train your neural ODE: the world of Jacobian and kinetic regularization C Finlay, JH Jacobsen, L Nurbekyan, A Oberman International conference on machine learning, 3154-3164, 2020 | 288* | 2020 |
Scaleable input gradient regularization for adversarial robustness C Finlay, AM Oberman Machine Learning with Applications 3, 100017, 2021 | 74 | 2021 |
Are more complicated tumour control probability models better? J Gong, MM Dos Santos, C Finlay, T Hillen Mathematical medicine and biology: a journal of the IMA 30 (1), 1-19, 2013 | 56 | 2013 |
Lipschitz regularized deep neural networks generalize and are adversarially robust C Finlay, J Calder, B Abbasi, A Oberman arXiv preprint arXiv:1808.09540, 2018 | 46 | 2018 |
From cell population models to tumor control probability: including cell cycle effects T Hillen, GDA De VrIeS, J Gong, C Finlay Acta Oncologica 49 (8), 1315-1323, 2010 | 37 | 2010 |
The logbarrier adversarial attack: making effective use of decision boundary information C Finlay, AA Pooladian, A Oberman Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 35 | 2019 |
Learning normalizing flows from Entropy-Kantorovich potentials C Finlay, A Gerolin, AM Oberman, AA Pooladian arXiv preprint arXiv:2006.06033, 2020 | 26 | 2020 |
Improved robustness to adversarial examples using Lipschitz regularization of the loss C Finlay, AM Oberman, B Abbasi | 21 | 2018 |
Annual ring density for lodgepole pine as derived from models for earlywood density, latewood density and latewood proportion DF Sattler, C Finlay, JD Stewart Forestry: An International Journal of Forest Research 88 (5), 622-632, 2015 | 12 | 2015 |
Approximate homogenization of convex nonlinear elliptic PDEs C Finlay, AM Oberman Communications in Mathematical Sciences 16 (7), 1895 - 1906, 2018 | 9 | 2018 |
Multi-resolution continuous normalizing flows V Voleti, C Finlay, A Oberman, C Pal Annals of Mathematics and Artificial Intelligence, 1-23, 2024 | 7 | 2024 |
A principled approach for generating adversarial images under non-smooth dissimilarity metrics AA Pooladian, C Finlay, T Hoheisel, A Oberman International Conference on Artificial Intelligence and Statistics, 1442-1452, 2020 | 7 | 2020 |
Improved accuracy of monotone finite difference schemes on point clouds and regular grids C Finlay, A Oberman SIAM Journal on Scientific Computing 41 (5), A3097-A3117, 2019 | 7 | 2019 |
Approximate homogenization of fully nonlinear elliptic PDEs: estimates and numerical results for Pucci type equations C Finlay, AM Oberman Journal of Scientific Computing 77, 936-949, 2018 | 6 | 2018 |
Empirical confidence estimates for classification by deep neural networks C Finlay, AM Oberman | 3 | 2019 |
Three gaps for quantisation in learned image compression S Pan, C Finlay, C Besenbruch, W Knottenbelt Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 2 | 2021 |
Method and data processing system for lossy image or video encoding, transmission and decoding C Finlay, J Rayner, C Besenbruch, A Zafar US Patent 11,544,881, 2023 | 1 | 2023 |
Improving continuous normalizing flows using a multi-resolution framework V Voleti, C Finlay, AM Oberman, C Pal ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit …, 2021 | 1 | 2021 |
Deterministic Gaussian Averaged Neural Networks R Campbell, C Finlay, AM Oberman arXiv preprint arXiv:2006.06061, 2020 | 1 | 2020 |
Calibrated Top-1 Uncertainty estimates for classification by score based models AM Oberman, C Finlay, A Iannantuono, T Salvador arXiv preprint arXiv:1903.09215, 2019 | 1 | 2019 |