In this talk, we will present continuum limits of residual neural networks, Neural ODEs. We will show some of their properties and later address some of their applications such as normalising flows. The motivational problem will be to match one probability density to another using a time dependent vector field given by a simple neural network. We will make analogies with classical rearrengements in transportation theory.
Camille Labourie