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Learning Differential Equations that are Easy to Solve

9 July 2020

Papers citing "Learning Differential Equations that are Easy to Solve"

24 / 24 papers shown

Title
Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flow Carlos E. Pérez De Jesús Alec J. Linot Michael D. Graham AI4CE 81 2 0 15 Dec 2023
Hypersolvers: Toward Fast Continuous-Depth Models Michael Poli Stefano Massaroli Atsushi Yamashita Hajime Asama Jinkyoo Park BDL AI4CE 54 47 0 19 Jul 2020
Interpolation between Residual and Non-Residual Networks Zonghan Yang Yang Liu Chenglong Bao Zuoqiang Shi 47 13 0 10 Jun 2020
OT-Flow: Fast and Accurate Continuous Normalizing Flows via Optimal Transport Derek Onken Samy Wu Fung Xingjian Li Lars Ruthotto OT 62 161 0 29 May 2020
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows Derek Onken Lars Ruthotto BDL 75 54 0 27 May 2020
Neural Controlled Differential Equations for Irregular Time Series Patrick Kidger James Morrill James Foster Terry Lyons AI4TS 111 481 0 18 May 2020
Neural Differential Equations for Single Image Super-resolution Teven Le Scao 31 2 0 02 May 2020
Differentiable Molecular Simulations for Control and Learning Wujie Wang Simon Axelrod Rafael Gómez-Bombarelli AI4CE 183 49 0 27 Feb 2020
Dissecting Neural ODEs Stefano Massaroli Michael Poli Jinkyoo Park Atsushi Yamashita Hajime Asama 99 204 0 19 Feb 2020
How to train your neural ODE: the world of Jacobian and kinetic regularization Chris Finlay J. Jacobsen L. Nurbekyan Adam M. Oberman 64 301 0 07 Feb 2020
A Machine Learning Framework for Solving High-Dimensional Mean Field Game and Mean Field Control Problems Lars Ruthotto Stanley Osher Wuchen Li L. Nurbekyan Samy Wu Fung AI4CE 120 220 0 04 Dec 2019
Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands H. Pinckaers G. Litjens SSeg MedIm 113 36 0 23 Oct 2019
On Robustness of Neural Ordinary Differential Equations Hanshu Yan Jiawei Du Vincent Y. F. Tan Jiashi Feng OOD 82 141 0 12 Oct 2019
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series E. Brouwer Jaak Simm Adam Arany Yves Moreau SyDa CML AI4TS 96 299 0 29 May 2019
Augmented Neural ODEs Emilien Dupont Arnaud Doucet Yee Whye Teh BDL 150 632 0 02 Apr 2019
DiffEqFlux.jl - A Julia Library for Neural Differential Equations Christopher Rackauckas Mike Innes Yingbo Ma J. Bettencourt Lyndon White Vaibhav Dixit 69 119 0 06 Feb 2019
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models Will Grathwohl Ricky T. Q. Chen J. Bettencourt Ilya Sutskever David Duvenaud DRL 154 881 0 02 Oct 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 429 5,157 0 19 Jun 2018
Deep Neural Networks Motivated by Partial Differential Equations Lars Ruthotto E. Haber AI4CE 124 491 0 12 Apr 2018
Sensitivity and Generalization in Neural Networks: an Empirical Study Roman Novak Yasaman Bahri Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein AAML 95 441 0 23 Feb 2018
Multi-level Residual Networks from Dynamical Systems View B. Chang Lili Meng E. Haber Frederick Tung David Begert 79 172 0 27 Oct 2017
Masked Autoregressive Flow for Density Estimation George Papamakarios Theo Pavlakou Iain Murray 215 1,360 0 19 May 2017
Maximum a Posteriori State Path Estimation: Discretization Limits and their Interpretation D. A. Dutra B. Teixeira L. A. Aguirre 37 32 0 20 Mar 2014
Reproducing kernel Hilbert spaces of Gaussian priors Van der Vaart V. Zanten 94 224 0 21 May 2008