We introduce a new family of deep neural network models. Instead of specifying a discrete
sequence of hidden layers, we parameterize the derivative of the hidden state using a …

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 …

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 …

Differential equations parameterized by neural networks become expensive to solve
numerically as training progresses. We propose a remedy that encourages learned …

Abstract Neural Ordinary Differential Equations (Neural ODEs) are the continuous analog of
Residual Neural Networks (ResNets). We investigate whether the discrete dynamics defined …

DiffEqFlux. jl is a library for fusing neural networks and differential equations. In this work we
describe differential equations from the viewpoint of data science and discuss the …

Continuous-time neural networks are a class of machine learning systems that can tackle
representation learning on spatiotemporal decision-making tasks. These models are …

Residual neural networks can be viewed as the forward Euler discretization of an Ordinary
Differential Equation (ODE) with a unit time step. This has recently motivated researchers to …

Originally inspired by neurobiology, deep neural network models have become a powerful
tool of machine learning and artificial intelligence. They can approximate functions and …