On Robustness of Neural Ordinary Differential Equations

Understand

Neural ordinary differential equations (ODEs) have been attracting increasing attention in various research domains recently.

• There have been some works studying optimization issues and approximation capabilities of neural ODEs, but their robustness is still yet unclear.
• In this work, we fill this important gap by exploring robustness properties of neural ODEs both empirically and theoretically.
• We first present an empirical study on the robustness of the neural ODE-based networks (ODENets) by exposing them to inputs with various types of perturbations and subsequently investigating the changes of the corresponding outputs.