Learning Continuous System Dynamics from Irregularly-Sampled Partial
Observations

Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations

8 November 2020

Papers citing "Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations"

17 / 17 papers shown

Title
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks Ziqiang Liu Xiaoda Wang Bohan Wang Zijie Huang Carl Yang Wei-dong Jin AI4TS AI4CE 197 1 0 29 Mar 2025
Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation Weizhi Zhang Liangwei Yang Zihe Song Henry Peng Zou Ke Xu Liancheng Fang Philip S. Yu GNN 39 1 0 26 Jul 2024
Learning System Dynamics without Forgetting Xikun Zhang Dongjin Song Yushan Jiang Yixin Chen Dacheng Tao AI4CE 39 2 0 30 Jun 2024
CASPER: Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation Baoyu Jing Dawei Zhou Kan Ren Carl Yang CML AI4TS 32 7 0 18 Mar 2024
Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-time Dynamics Lanlan Chen K. Wu Jian Lou Jing Liu 37 6 0 18 Dec 2023
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers Maciej Besta Afonso Claudino Catarino Lukas Gianinazzi Nils Blach Piotr Nyczyk H. Niewiadomski Torsten Hoefler 35 6 0 30 Nov 2023
COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning Chuizheng Meng Yihe Dong Sercan Ö. Arik Yan Liu Tomas Pfister CML AI4TS 29 0 0 01 Nov 2023
Boosting long-term forecasting performance for continuous-time dynamic graph networks via data augmentation Yu Tian Mingjie Zhu Jiachi Luo Song Li 32 0 0 12 Apr 2023
Towards Better Dynamic Graph Learning: New Architecture and Unified Library Le Yu Leilei Sun Bowen Du Weifeng Lv AI4CE 29 98 0 23 Mar 2023
MTP-GO: Graph-Based Probabilistic Multi-Agent Trajectory Prediction with Neural ODEs Theodor Westny Joel Oskarsson Björn Olofsson Erik Frisk 48 32 0 01 Feb 2023
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations Ivan Marisca Andrea Cini Cesare Alippi AI4TS 37 62 0 26 May 2022
Heavy Ball Neural Ordinary Differential Equations Hedi Xia Vai Suliafu H. Ji T. Nguyen Andrea L. Bertozzi Stanley J. Osher Bao Wang 38 56 0 10 Oct 2021
Heterogeneous Graph Transformer Ziniu Hu Yuxiao Dong Kuansan Wang Yizhou Sun 185 1,170 0 03 Mar 2020
A Compositional Object-Based Approach to Learning Physical Dynamics Michael Chang T. Ullman Antonio Torralba J. Tenenbaum AI4CE OCL 241 438 0 01 Dec 2016
Interaction Networks for Learning about Objects, Relations and Physics Peter W. Battaglia Razvan Pascanu Matthew Lai Danilo Jimenez Rezende Koray Kavukcuoglu AI4CE OCL PINN GNN 283 1,401 0 01 Dec 2016
Recurrent Neural Networks for Multivariate Time Series with Missing Values Zhengping Che S. Purushotham Kyunghyun Cho David Sontag Yan Liu AI4TS 246 1,900 0 06 Jun 2016
Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization Linhong Zhu Dong Guo Junming Yin Greg Ver Steeg Aram Galstyan 51 199 0 13 Nov 2014