Universal Approximation Property of Neural Ordinary Differential
Equations

Universal Approximation Property of Neural Ordinary Differential Equations

4 December 2020

Takeshi Teshima

Papers citing "Universal Approximation Property of Neural Ordinary Differential Equations"

12 / 12 papers shown

Title
Feedback Favors the Generalization of Neural ODEs Jindou Jia Zihan Yang Meng Wang Kexin Guo Jianfei Yang Xiang Yu Lei Guo OOD AI4CE 43 2 0 14 Oct 2024
Deep Koopman-layered Model with Universal Property Based on Toeplitz Matrices Yuka Hashimoto Tomoharu Iwata 28 0 0 03 Oct 2024
Deep Curvilinear Editing: Commutative and Nonlinear Image Manipulation for Pretrained Deep Generative Model Takehiro Aoshima Takashi Matsubara 47 4 0 26 Nov 2022
Whitening Convergence Rate of Coupling-based Normalizing Flows Felix Dräxler Christoph Schnörr Ullrich Kothe 36 7 0 25 Oct 2022
Dynamical systems' based neural networks E. Celledoni Davide Murari B. Owren Carola-Bibiane Schönlieb Ferdia Sherry OOD 46 12 0 05 Oct 2022
Do Residual Neural Networks discretize Neural Ordinary Differential Equations? Michael E. Sander Pierre Ablin Gabriel Peyré 35 25 0 29 May 2022
Learning via nonlinear conjugate gradients and depth-varying neural ODEs George Baravdish Gabriel Eilertsen Rym Jaroudi B. Johansson Lukávs Malý Jonas Unger 24 3 0 11 Feb 2022
Neural Information Squeezer for Causal Emergence Jiang Zhang Kaiwei Liu CML 30 14 0 25 Jan 2022
Neural Flows: Efficient Alternative to Neural ODEs Marin Bilovs Johanna Sommer Syama Sundar Rangapuram Tim Januschowski Stephan Günnemann AI4TS 33 70 0 25 Oct 2021
Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory Takashi Matsubara Yuto Miyatake Takaharu Yaguchi 23 23 0 19 Feb 2021
Momentum Residual Neural Networks Michael E. Sander Pierre Ablin Mathieu Blondel Gabriel Peyré 27 57 0 15 Feb 2021
Neural Ordinary Differential Equation Control of Dynamics on Graphs Thomas Asikis Lucas Böttcher Nino Antulov-Fantulin 33 43 0 17 Jun 2020