Stochastic Localization

Stochastic localization focuses on refining the accuracy of estimations or samplings from noisy data, particularly in high-dimensional spaces. Current research emphasizes developing efficient algorithms, such as those based on diffusion models and iterative posterior sampling, to mitigate the effects of noise and improve the convergence rate of these estimations. These advancements are crucial for various applications, including sampling from complex probability distributions, solving challenging physics problems like finding localized eigenstates in disordered media, and improving the accuracy of semi-supervised object detection by addressing localization noise in pseudo-labels. The resulting improvements in accuracy and efficiency have significant implications across diverse scientific fields and practical applications.