Normalizing flow sampling with Langevin dynamics in the latent space

Understand

Normalizing flows (NF) use a continuous generator to map a simple latent (e.g.

• Gaussian) distribution, towards an empirical target distribution associated with a training data set.
• Once trained by minimizing a variational objective, the learnt map provides an approximate generative model of the target distribution.
• Since standard NF implement differentiable maps, they may suffer from pathological behaviors when targeting complex distributions.