Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About
About me
publications
From cell population models to tumor control probability: including cell cycle effects
Published in Acta Oncologica, 2010
Recommended citation: Hillen, Thomas, Gerda De Vries, Jiafen Gong, and Chris Finlay. "From cell population models to tumor control probability: including cell cycle effects." Acta Oncologica 49, no. 8 (2010): 1315-1323.
Are more complicated tumour control probability models better?
Published in Mathematical Medicine and Biology, 2013
Recommended citation: Gong, Jiafen, Mairon M. Dos Santos, Chris Finlay, and Thomas Hillen. "Are more complicated tumour control probability models better?" Mathematical Medicine and Biology: a journal of the IMA 30, no. 1 (2011): 1-19.
Annual ring density for lodgepole pine as derived from models for earlywood density, latewood density and latewood proportion
Published in Forestry: An International Journal of Forest Research, 2015
Recommended citation: Sattler, Derek F., Chris Finlay, and James D. Stewart. "Annual ring density for lodgepole pine as derived from models for earlywood density, latewood density and latewood proportion." Forestry: An International Journal of Forest Research 88, no. 5 (2015): 622-632.
Approximate homogenization of fully nonlinear elliptic PDEs: estimates and results for Pucci type equations
Published in Journal of Scientific Computing, 2018
Recommended citation: Finlay, Chris, and Adam M. Oberman. "Approximate homogenization of fully nonlinear elliptic PDEs: estimates and results for Pucci type equations." Journal of Scientific Computing 77, no. 2 (2018): 936-949.
Approximate homogenization of convex nonlinear elliptic PDEs
Published in Communications in Mathematical Sciences, 2018
Recommended citation: Finlay, Chris, and Adam M. Oberman. "Approximate homogenization of convex nonlinear elliptic PDEs." Communications in Mathematical Sciences 16, no. 7 (2018): 1895-1906.
Lipschitz regularized deep neural networks generalize and are adversarially robust
Published in , 2019
Recommended citation: Finlay, Chris, Jeff Calder, Bilal Abbasi, and Adam M. Oberman. "Lipschitz regularized deep neural networks generalize and are adversarially robust." arXiv preprint arXiv:1808.09540 (2019).
Improved accuracy of monotone finite difference schemes on point clouds and regular grids
Published in SIAM Journal on Scientific Computing (SISC), 2019
Recommended citation: Finlay, Chris, and Adam M. Oberman. "Improved accuracy of monotone finite difference schemes on point clouds and regular grids." SIAM Journal on Scientific Computing 49, no. 15 (2019): 3097-3117.
The LogBarrier adversarial attack: making effective use of decision boundary information
Published in International Conference on Computer Vision (ICCV), 2019
Recommended citation: Finlay, Chris, Aram-Alexandre Pooladian and Adam M. Oberman. "The LogBarrier adversarial attack: making effective use of decision boundary information." International Conference on Computer Vision, 2019.
A principled approach for generating adversarial images under non-smooth dissimilarity metrics
Published in Artificial Intelligence and Statistics (AISTATS), 2020
Recommended citation: Aram-Alexandre Pooladian, Chris Finlay, Tim Hoheisel, and Adam M. Oberman. "A principled approach for generating adversarial images under non-smooth dissimilarity metrics." 23rd International Conference on Artificial Intelligence and Statistics (2020).
Deterministic Gaussian Averaged Neural Networks
Published in , 2020
Recommended citation: Campbell, Ryan, Chris Finlay, and Adam M. Oberman. "Deterministic Gaussian Averaged Neural Networks." arXiv preprint arXiv:2006.06061 (2020).
How to train your Neural ODE: the world of Jacobian and kinetic regularization
Published in International Conference on Machine Learning (ICML), 2020
Recommended citation: Finlay, Chris, Jörn-Henrik Jacobsen, Levon Nurbekyan, and Adam M. Oberman. "How to train your Neural ODE: the world of Jacobian and kinetic regularization." International Conference on Machine Learning, 2020..
Learning normalizing flows from Entropy-Kantorovich potentials
Published in ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (Contributed talk), 2020
Recommended citation: Finlay, Chris, Augusto Gerolin, Adam M. Oberman, and Aram-Alexandre Pooladian. "Learning normalizing flows from Entropy-Kantorovich potentials." arXiv preprint arXiv:2006.06033 (2020).
Scaleable input gradient regularization for adversarial robustness
Published in Machine Learning with Applications, 2021
Recommended citation: Finlay, Chris, and Adam M. Oberman. "Scaleable input gradient regularization for adversarial robustness." Machine Learning with Applications 3 (2021): 100017.