On Second Order Behaviour in Augmented Neural ODEs

12 June 2020

Papers citing "On Second Order Behaviour in Augmented Neural ODEs"

50 / 50 papers shown

Title
On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning Alvaro Arroyo Alessio Gravina Benjamin Gutteridge Federico Barbero Claudio Gallicchio Xiaowen Dong Michael M. Bronstein P. Vandergheynst 42 2 0 15 Feb 2025
Understanding and Mitigating Membership Inference Risks of Neural Ordinary Differential Equations Sanghyun Hong Fan Wu A. Gruber Kookjin Lee 42 0 0 12 Jan 2025
Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary Differential Equations Yi Zhang Chun-Wun Cheng Junyi He Zhihai He Carola-Bibiane Schonlieb Yuyan Chen Angelica I Aviles-Rivero AI4TS 86 0 0 20 Dec 2024
ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence Wenjie Mei Dongzhe Zheng Shihua Li AI4CE 34 2 0 04 Nov 2024
Neural Network Emulator for Atmospheric Chemical ODE Zhi-Song Liu Petri S. Clusius Michael Boy 42 3 0 03 Aug 2024
System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization Jixiang Qing Becky D Langdon Robert M. Lee B. Shafei Mark van der Wilk Calvin Tsay Ruth Misener 37 1 0 04 Jun 2024
Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching Fernando Moreno-Pino Alvaro Arroyo H. Waldon Xiaowen Dong Álvaro Cartea AI4TS 34 1 0 31 May 2024
The Disappearance of Timestep Embedding in Modern Time-Dependent Neural Networks Bum Jun Kim Yoshinobu Kawahara Sang Woo Kim AI4TS 23 1 0 23 May 2024
Marrying Causal Representation Learning with Dynamical Systems for Science Dingling Yao Caroline Muller Francesco Locatello CML AI4CE 42 6 0 22 May 2024
Mechanistic Neural Networks for Scientific Machine Learning Adeel Pervez Francesco Locatello E. Gavves PINN AI4CE 19 8 0 20 Feb 2024
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations Woojin Cho Seunghyeon Cho Hyundong Jin Jinsung Jeon Kookjin Lee Sanghyun Hong Dongeun Lee Jonghyun Choi Noseong Park AI4TS AI4CE 18 2 0 16 Dec 2023
On Tuning Neural ODE for Stability, Consistency and Faster Convergence Sheikh Waqas Akhtar ODL 24 0 0 04 Dec 2023
Fourier Neural Differential Equations for learning Quantum Field Theories Isaac Brant Alexander Norcliffe Pietro Lió 17 0 0 28 Nov 2023
The Missing U for Efficient Diffusion Models Sergio Calvo-Ordoñez Chun-Wun Cheng Jiahao Huang Lipei Zhang Guang Yang Carola-Bibiane Schonlieb Angelica I Aviles-Rivero DiffM 33 4 0 31 Oct 2023
Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes Jiaxu Cui Bing Sun Jiming Liu Bo Yang 20 1 0 25 Oct 2023
SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases Yang Liu Jiashun Cheng Haihong Zhao Tingyang Xu P. Zhao Fugee Tsung Jia Li Yu Rong AI4CE 40 18 0 25 Aug 2023
Faster Training of Neural ODEs Using Gauß-Legendre Quadrature Alexander Norcliffe M. Deisenroth 23 3 0 21 Aug 2023
Fading memory as inductive bias in residual recurrent networks I. Dubinin Felix Effenberger 43 4 0 27 Jul 2023
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints Alistair J R White Niki Kilbertus Maximilian Gelbrecht Niklas Boers 20 6 0 16 Jun 2023
Neural Oscillators are Universal S. Lanthaler T. Konstantin Rusch Siddhartha Mishra 27 10 0 15 May 2023
Modulated Neural ODEs I. Auzina Çağatay Yıldız Sara Magliacane Matthias Bethge E. Gavves 30 5 0 26 Feb 2023
Learning PDE Solution Operator for Continuous Modeling of Time-Series Yesom Park Jaemoo Choi Changyeon Yoon Changhoon Song Myung-joo Kang AI4TS AI4CE 27 3 0 02 Feb 2023
Sparsity in Continuous-Depth Neural Networks H. Aliee Till Richter Mikhail Solonin I. Ibarra Fabian J. Theis Niki Kilbertus 29 10 0 26 Oct 2022
Reachability Analysis of a General Class of Neural Ordinary Differential Equations Diego Manzanas Lopez Patrick Musau Nathaniel P. Hamilton Taylor T. Johnson 23 14 0 13 Jul 2022
AdamNODEs: When Neural ODE Meets Adaptive Moment Estimation Suneghyeon Cho Sanghyun Hong Kookjin Lee Noseong Park 28 2 0 13 Jul 2022
ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase Transition Yuelin Wang Kai Yi Xinliang Liu Yu Guang Wang Shi Jin 16 33 0 11 Jun 2022
Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs Mona Buisson-Fenet V. Morgenthaler Sebastian Trimpe F. D. Meglio 46 6 0 25 May 2022
Neural ODEs with Irregular and Noisy Data P. Goyal P. Benner 26 4 0 19 May 2022
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs Justin Baker Hedi Xia Yiwei Wang E. Cherkaev A. Narayan Long Chen Jack Xin Andrea L. Bertozzi Stanley J. Osher Bao Wang 14 6 0 19 Apr 2022
Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs Justin Baker E. Cherkaev A. Narayan Bao Wang AI4CE 13 4 0 24 Feb 2022
Characteristic Neural Ordinary Differential Equations Xingzi Xu Ali Hasan Khalil Elkhalil Jie Ding Vahid Tarokh BDL 29 3 0 25 Nov 2021
Constructing Neural Network-Based Models for Simulating Dynamical Systems Christian Møldrup Legaard Thomas Schranz G. Schweiger Ján Drgovna Basak Falay C. Gomes Alexandros Iosifidis M. Abkar P. Larsen PINN AI4CE 33 93 0 02 Nov 2021
SE(3) Equivariant Graph Neural Networks with Complete Local Frames Weitao Du He Zhang Yuanqi Du Qi Meng Wei Chen Bin Shao Tie-Yan Liu 56 79 0 26 Oct 2021
Neural Flows: Efficient Alternative to Neural ODEs Marin Bilovs Johanna Sommer Syama Sundar Rangapuram Tim Januschowski Stephan Günnemann AI4TS 18 70 0 25 Oct 2021
How Does Momentum Benefit Deep Neural Networks Architecture Design? A Few Case Studies Bao Wang Hedi Xia T. Nguyen Stanley Osher AI4CE 39 10 0 13 Oct 2021
Heavy Ball Neural Ordinary Differential Equations Hedi Xia Vai Suliafu H. Ji T. Nguyen Andrea L. Bertozzi Stanley J. Osher Bao Wang 32 56 0 10 Oct 2021
Modular Neural Ordinary Differential Equations Max Zhu P. Lio Jacob Moss PINN 37 2 0 15 Sep 2021
m-RevNet: Deep Reversible Neural Networks with Momentum Duo Li Shangqi Gao 16 5 0 12 Aug 2021
Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations Xing Chen Flavio Abreu Araujo M. Riou J. Torrejon D. Ravelosona W. Kang Weisheng Zhao Julie Grollier D. Querlioz 28 40 0 23 Jul 2021
Learning ODEs via Diffeomorphisms for Fast and Robust Integration Weiming Zhi Tin Lai Lionel Ott Edwin V. Bonilla Fabio Ramos OOD 23 4 0 04 Jul 2021
Beyond Predictions in Neural ODEs: Identification and Interventions H. Aliee Fabian J. Theis Niki Kilbertus CML 40 24 0 23 Jun 2021
Continuous-Depth Neural Models for Dynamic Graph Prediction Michael Poli Stefano Massaroli Clayton M. Rabideau Junyoung Park Atsushi Yamashita Hajime Asama Jinkyoo Park AI4CE 9 8 0 22 Jun 2021
Neural Controlled Differential Equations for Online Prediction Tasks James Morrill Patrick Kidger Lingyi Yang Terry Lyons AI4TS 30 41 0 21 Jun 2021
Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics Shiqi Gong Qi Meng Yue Wang Lijun Wu Wei Chen Zhi-Ming Ma Tie-Yan Liu 13 2 0 08 Jun 2021
Meta-learning using privileged information for dynamics Ben Day Alexander Norcliffe Jacob Moss Pietro Lió 21 2 0 29 Apr 2021
Neural ODE Processes Alexander Norcliffe Cristian Bodnar Ben Day Jacob Moss Pietro Lió BDL AI4TS 33 63 0 23 Mar 2021
Momentum Residual Neural Networks Michael E. Sander Pierre Ablin Mathieu Blondel Gabriel Peyré 24 56 0 15 Feb 2021
Neural SDEs as Infinite-Dimensional GANs Patrick Kidger James Foster Xuechen Li Harald Oberhauser Terry Lyons DiffM 11 141 0 06 Feb 2021
STEER: Simple Temporal Regularization For Neural ODEs Arna Ghosh Harkirat Singh Behl Emilien Dupont Philip H. S. Torr Vinay P. Namboodiri BDL AI4TS 27 74 0 18 Jun 2020
Convolutional Neural Networks combined with Runge-Kutta Methods Mai Zhu Bo Chang Chong Fu AI4CE 35 52 0 24 Feb 2018