Neural Ordinary Differential Equations for Intervention Modeling

• URL: http://arxiv.org/abs/2010.08304v1
• Date: Fri, 16 Oct 2020 10:55:12 GMT
• Title: Neural Ordinary Differential Equations for Intervention Modeling
• Authors: Daehoon Gwak, Gyuhyeon Sim, Michael Poli, Stefano Massaroli, Jaegul Choo, Edward Choi
• Abstract summary: Real-world systems often involve external interventions that cause changes in the system dynamics. Neural ODE and a number of its recent variants are not suitable for modeling such interventions as they do not properly model the observations and the interventions separately. 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.
• Score: 30.127870899307254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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