Speaker
Yi-Zhuang You
(University of California, San Diego)
Description
Abstract: Generative modeling in machine learning can be understood as the inverse process of renormalization, establishing a profound connection between lattice field theory and modern AI techniques. This perspective allows the development of unsupervised learning algorithms that inherently learn to perform renormalization from the field theory action. In this talk, I will explore how flow-based generative models, inspired by AdS-CFT duality, can accelerate the sampling of conformal field theories (CFTs) using network architectures defined on the AdS bulk geometry. Furthermore, I will discuss how these concepts extend to image generation, enabling more efficient error correction through holographic principles.
