Neural ODE Processes

Understand

Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system.

• However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages.
• First, they are unable to adapt to incoming data points, a fundamental requirement for real-time applications imposed by the natural direction of time.
• Second, time series are often composed of a sparse set of measurements that could be explained by many possible underlying dynamics.