| Abstract |
Our approach relies on input-convex neural networks (ICNNs) to discretize the
JKO steps, which can be optimized by stochastic gradient descent. Unlike previous
work, our method does not require domain discretization or particle simulation.
As a result, we can sample from the measure at each time step of the diffusion
and compute its probability density. We demonstrate our algorithm’s performance
by computing diffusions following the Fokker-Planck equation and apply it to
unnormalized density sampling as well as nonlinear filtering. |
