Chris Finlay
Chris Finlay
Deep Render
Verified email at deeprender.ai - Homepage
Title
Cited by
Cited by
Year
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
412013
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
342010
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
27*2020
The logbarrier adversarial attack: making effective use of decision boundary information
C Finlay, AA Pooladian, A Oberman
Proceedings of the IEEE International Conference on Computer Vision, 4862-4870, 2019
102019
Scaleable input gradient regularization for adversarial robustness
C Finlay, AM Oberman
arXiv preprint arXiv:1905.11468, 2019
92019
Improved robustness to adversarial examples using Lipschitz regularization of the loss
C Finlay, AM Oberman, B Abbasi
72018
Lipschitz regularized deep neural networks generalize and are adversarially robust
C Finlay, J Calder, B Abbasi, A Oberman
arXiv preprint arXiv:1808.09540, 2018
72018
Empirical confidence estimates for classification by deep neural networks
C Finlay, AM Oberman
42019
Approximate homogenization of convex nonlinear elliptic PDEs
C Finlay, AM Oberman
Communications in Mathematical Sciences 16 (7), 1895 - 1906, 2018
42018
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
42015
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
22020
Approximate homogenization of fully nonlinear elliptic PDEs: estimates and numerical results for Pucci type equations
C Finlay, AM Oberman
Journal of Scientific Computing 77 (2), 936-949, 2018
22018
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
12019
Adversarial Boot Camp: label free certified robustness in one epoch
R Campbell, C Finlay, AM Oberman
arXiv preprint arXiv:2010.02508, 2020
2020
Deterministic Gaussian Averaged Neural Networks
R Campbell, C Finlay, AM Oberman
arXiv preprint arXiv:2006.06061, 2020
2020
Learning normalizing flows from Entropy-Kantorovich potentials
C Finlay, A Gerolin, AM Oberman, AA Pooladian
arXiv preprint arXiv:2006.06033, 2020
2020
Farkas layers: don't shift the data, fix the geometry
AA Pooladian, C Finlay, AM Oberman
arXiv preprint arXiv:1910.02840, 2019
2019
On some applied problems using nonlinear elliptic PDEs
C Finlay
2019
Empirical uncertainty estimates for classification by deep neural networks
C Finlay, AM Oberman
arXiv preprint arXiv:1903.09215, 2019
2019
Calibrated top-1 uncertainty estimates for classification by score based models
AM Oberman, C Finlay, A Iannantuono, T Salvador
arXiv e-prints, arXiv: 1903.09215, 2019
2019
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