2020/10/16 by Daehoon Gwak, Gwak, Daehoon, Gyuhyeon Sim +9 · 1 citation
Computer Science · Engineering · #Control Systems and Identification #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Healthcare #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.2010.08304
openalex publication_date 2020/10/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
By interpreting the forward dynamics of the latent representation of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation (Neural ODE) emerged as an effective framework for modeling a system dynamics in the continuous time domain. However, real-world systems often involves external interventions that cause changes in the system dynamics such as a moving ball coming in contact with another ball, or such as a patient being administered with particular drug. Neural ODE and a number of its recent variants, however, are not suitable for modeling such interventions as they do not properly model the observations and the interventions separately. In this paper, we propose a novel neural ODE-based approach (IMODE) that properly model the effect of external interventions by employing two ODE functions to separately handle the observations and the interventions. Using both synthetic and real-world time-series datasets involving interventions, our experimental results consistently demonstrate the superiority of IMODE compared to existing approaches.