Hello everyone ! Mark Goldstein is visiting us to give a talk about his research on diffusion models this Friday 11 AM — 12 PM at Friend Center 0 0 8 . Details below . Abstract: This talk will consist of two parts . First , we will discuss continuous diffusion models and the role of noising processes and base distributions , which govern the sequence of densities that a diffusion model must traverse to generate data . These details in turn govern problem difficulty . We will discuss choices that can improve models and reduce the amount of optimization required , in the context of natural images , video generation , Navier -Stokes equations , and protein design . We will then see model likelihood bounds that unify several variants of diffusion models , flow matching , etc . . . Second , we will discuss discrete diffusion models , which have been around in varying forms for a few years but have recently been formalized as proper non -autoregressive generative models for discrete data like text , images with integer pixels , atomic spins , and amino acid sequences . For discrete diffusion models , we will discuss limitations in current practice (sub -optimal factorization choices ) and possible routes and challenges involved in improving the models . Bio: Mark Goldstein is a PhD candidate in Computer Science at New York University working with Rajesh Ranganath , and a current Student Researcher at Google Deepmind . Mark 's work focuses on deep generative models for high -dimensional generation problems , with applications in health and science . Core to this work is rethinking fundamental choices in diffusion model training , and investigating how they affect performance and efficiency . Mark has studied these models in the context of video generation , Navier -Stokes equations , and medical imaging data . Previously , Mark completed a Bachelor 's of Music Composition at New England Conservatory of Music . Best, Wenzhe, Ahmed & Chi 1.