Transition matching (TM) replaces the infinitesimal-timestep kernels from Flow Matching/Diffusion with a generative model, advancing both flow/diffusion and autoregressive models. TM variants achieve state-of-the-art text-to-image generation.
Neta Shaul is a PhD student at the Weizmann Institute of Science under the supervision of Prof. Yaron Lipman. His research focuses on developing and advancing scalable modeling frameworks for generative models. He is interested in a variety of data types from both discrete and continuous domains (text, images, videos, proteins, etc).