Special Artificial Intelligence Seminar
In Person and Virtual - ET - Gates Hillman 8102 and Zoom
YILUN DU , Ph.D. Student, Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology,
Learning to Generate Compositionally
Generative AI has led to stunning successes in recent years but is fundamentally limited by the amount of data available. In this talk, I’ll introduce the idea of compositional generative modeling, which can help avoid this issue by building complex generative models from smaller constituent components. First, I introduce the idea of energy-based models and illustrate how they enable compositional generative modeling. I’ll then illustrate how such compositionality can enable effective generalization, both to complex visual scenes and robotic actions unseen at training time. Finally, I’ll show how such compositionality can be applied to existing large “foundation models” to construct intelligent decision-making agents that can hierarchically plan and reason.
Yilun Du is a final year PhD student at MIT EECS advised by Prof. Leslie Kaelbling, Prof. Tomas Lozano-Perez and Prof. Joshua B. Tenenbaum. Previously, he was a research fellow at OpenAI, and an intern and visiting researcher at FAIR and Google DeepMind. His research focuses on generative models, decision making, robot learning, 3D vision, embodied agents and the applications of such tools to scientific domains.