… We introduce a new family of deep neural network models. Instead of specifying a discrete
… neural network. The output of the network is computed using a blackbox differential equation …

… Neural ordinary differential equations (Chen et al.… 2018) form a family of models that
approximate nonlinear mappings by using continuous-time ODEs. Due to their desirable properties, …

… 2018), we perform an instantaneous jump of H at each time tk using the next input features
Xtk . Let LGtkbe the graph Laplacian of graph Gtk , which can computed in several ways (…

… We extend Neural ODEs to implicitly defined termination criteria modeled by neural event …
2018) provides an identity that quantifies the instantaneous change in the adjoint state: …

… Models proposed in this paper use a neural ordinary function odeRNNCell to compute
change or derivative of the hidden dynamics at any time t. odeRNNCell is usually an initial value …

… network scaling techniques, such as scaling the neural network inputs and batch-normalization.Given
the first order optimizers are solving an ordinary differential equation on the …

… of neural networks as an ordinary differential equation, Neural Ordinary Differential Equation
(Neural ODE) … Neural ODE and a number of its recent variants, however, are not suitable for …

… of ordinary differential equations through recurrent neural networks … as multilayer perceptrons
and recurrent neural networks) and optimization … Neural ordinary differential equations …

… be used to model neural networks. An ODE is referred to as a neural ordinary differential
equation (neuralODE) when it is used to describe the dynamics of a neural network. All the …

… forward Euler method to the ordinary differential equation (ODE): … 2018) it was shown that
adding more blocks of the PDE-based … Neural ordinary differential equations. In Advances in …