Continuous-Time Flows for Efficient Inference and Density Estimation

Understand

Two fundamental problems in unsupervised learning are efficient inference for latent-variable models and robust density estimation based on large amounts of unlabeled data.

• Algorithms for the two tasks, such as normalizing flows and generative adversarial networks (GANs), are often developed independently.
• In this paper, we propose the concept of {\em continuous-time flows} (CTFs), a family of diffusion-based methods that are able to asymptotically approach a target distribution.
• Distinct from normalizing flows and GANs, CTFs can be adopted to achieve the above two goals in one framework, with theoretical guarantees.