Variational Inference with Continuously-Indexed Normalizing Flows

Continuously-indexed flows (CIFs) have recently achieved improvements over baseline normalizing flows in a variety of density estimation tasks. In this paper, we adapt CIFs to the task of variational inference (VI) through the framework of auxiliary VI, and demonstrate that the advantages of CIFs over baseline flows can also translate to the VI setting for both sampling from posteriors with complicated topology and performing maximum likelihood estimation in latent-variable models.