Neural ODEs as continuous network layers

In a previous article we talked about how to put neural networks inside ODEs to learn their dynamics from data. Armed with that knowledge we created a powerful weather forecasting model. But learning the dynamics of a process is only one side of the neural ODE story, they can also be used as very flexible function approximators much like regular neural network. In this article we are going to create continuous neural network layers using neural ODEs and see how they can be used to classify the Fashion MNIST dataset. Read more