Stochastic Localization via Iterative Posterior Sampling

Understand

Building upon score-based learning, new interest in stochastic localization techniques has recently emerged.

• In these models, one seeks to noise a sample from the data distribution through a stochastic process, called observation process, and progressively learns a denoiser associated to this dynamics.
• Apart from specific applications, the use of stochastic localization for the problem of sampling from an unnormalized target density has not been explored extensively.
• This work contributes to fill this gap.