| 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 | 300* | 2020 |
| Convergent difference schemes for degenerate elliptic and parabolic equations: Hamilton--Jacobi equations and free boundary problems AM Oberman SIAM Journal on Numerical Analysis 44 (2), 879-895, 2006 | 282 | 2006 |
| Numerical solution of the optimal transportation problem using the Monge–Ampère equation JD Benamou, BD Froese, AM Oberman Journal of Computational Physics 260, 107-126, 2014 | 269* | 2014 |
| Numerical methods for the fractional Laplacian: A finite difference-quadrature approach Y Huang, A Oberman SIAM Journal on Numerical Analysis 52 (6), 3056-3084, 2014 | 231 | 2014 |
| Wide stencil finite difference schemes for the elliptic Monge-Ampere equation and functions of the eigenvalues of the Hessian AM Oberman Discrete Contin. Dyn. Syst. Ser. B 10 (1), 221-238, 2008 | 186 | 2008 |
| Deep relaxation: partial differential equations for optimizing deep neural networks P Chaudhari, A Oberman, S Osher, S Soatto, G Carlier Research in the Mathematical Sciences 5, 1-30, 2018 | 174 | 2018 |
| A convergent difference scheme for the infinity Laplacian: construction of absolutely minimizing Lipschitz extensions A Oberman Mathematics of computation 74 (251), 1217-1230, 2005 | 168 | 2005 |
| Convergent finite difference solvers for viscosity solutions of the elliptic Monge-Ampere equation in dimensions two and higher BD Froese, AM Oberman SIAM J. Numer. Anal., 49 (4), 1692–1714, 2010 | 162 | 2010 |
| Two numerical methods for the elliptic Monge-Ampere equation JD Benamou, BD Froese, AM Oberman ESAIM: Mathematical Modelling and Numerical Analysis 44 (4), 737-758, 2010 | 160 | 2010 |
| Bulk Burning Rate in Passive–Reactive Diffusion P Constantin, A Kiselev, A Oberman, L Ryzhik Archive for rational mechanics and analysis 154 (1), 53-91, 2000 | 148 | 2000 |
| Numerical methods for matching for teams and Wasserstein barycenters G Carlier, A Oberman, E Oudet ESAIM: Mathematical Modelling and Numerical Analysis 49 (6), 1621-1642, 2015 | 132 | 2015 |
| Convergent filtered schemes for the Monge--Ampère partial differential equation BD Froese, AM Oberman SIAM Journal on Numerical Analysis 51 (1), 423-444, 2013 | 118 | 2013 |
| The convex envelope is the solution of a nonlinear obstacle problem A Oberman Proceedings of the American Mathematical Society 135 (6), 1689-1694, 2007 | 115 | 2007 |
| Lipschitz regularized deep neural networks generalize and are adversarially robust C Finlay, J Calder, B Abbasi, A Oberman arXiv preprint arXiv:1808.09540, 2018 | 100* | 2018 |
| The Dirichlet problem for the convex envelope A Oberman, L Silvestre Transactions of the American Mathematical Society 363 (11), 5871-5886, 2011 | 87 | 2011 |
| Fast finite difference solvers for singular solutions of the elliptic Monge–Ampère equation BD Froese, AM Oberman Journal of Computational Physics 230 (3), 818-834, 2011 | 83 | 2011 |
| An efficient linear programming method for optimal transportation AM Oberman, Y Ruan arXiv preprint arXiv:1509.03668, 2015 | 82 | 2015 |
| Anisotropic total variation regularized l^ 1-approximation and denoising/deblurring of 2d bar codes R Choksi, Y van Gennip, A Oberman arXiv preprint arXiv:1007.1035, 2010 | 80 | 2010 |
| Scaleable input gradient regularization for adversarial robustness C Finlay, AM Oberman Machine Learning with Applications 3, 100017, 2021 | 78 | 2021 |
| A convergent monotone difference scheme for motion of level sets by mean curvature AM Oberman Numerische Mathematik 99, 365-379, 2004 | 73 | 2004 |
Chris FinlayDeep RenderVerified email at deeprender.ai
