Abstract:

The success of Autoregressive (AR) models in language today is so tremendous that their scope has, in turn, been largely narrowed to specific instantiations. In this talk, we will revisit the foundations of classical AR models, discussing essential concepts that may have been overlooked in modern practice. We will then introduce our recent research on broadening the scope of modern AR models in the context of image generation, challenging the common beliefs about how AR models can be built. We will also discuss open questions and potential directions for future research.

Bio:

Kaiming He is an Associate Professor in the Department of EECS at MIT which he joined in Feb 2024. Before that, he was a research scientist in industrial labs including Facebook AI Research (FAIR, 2016-2024) and Microsoft Research (MSR, 2011-2016). His research covers a wide range of topics in Computer Vision and Machine Learning. His work has been recognized by numerous prestigious awards in the community, including the PAMI Young Researcher Award 2018 and multiple Best Paper Awards at top-tier conferences such as CVPR, ICCV, and ECCV.