Neural Ordinary Differential Equations (NODEs), a framework of continuous-depth neural
networks, have been widely applied, showing exceptional efficacy in coping with some …

Continuous-depth neural networks, such as the Neural Ordinary Differential Equations
(ODEs), have aroused a great deal of interest from the communities of machine learning and …

Neural ordinary differential equations (neural ODEs) are a popular family of continuous-
depth deep learning models. In this work, we consider a large family of parameterized ODEs …

Continuous deep learning architectures have recently re-emerged as Neural Ordinary
Differential Equations (Neural ODEs). This infinite-depth approach theoretically bridges the …

Since the last decade, deep neural networks have shown remarkable capability in learning
representations. The recently proposed neural ordinary differential equations (NODEs) can …

Neural ordinary differential equations (NODEs)--parametrizations of differential equations
using neural networks--have shown tremendous promise in learning models of unknown …

Abstract Neural Ordinary Differential Equations model dynamical systems with ODEs
learned by neural networks. However, ODEs are fundamentally inadequate to model …

The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep
networks where continuous time can replace the discrete notion of depth, ODE solvers …

We derive and solve an``Equation of Motion''(EoM) for deep neural networks (DNNs), a
differential equation that precisely describes the discrete learning dynamics of DNNs …