Yingzhen Li

Towards Causal Deep Generative Models for Sequential Data

One of my research dreams is to build a high-resolution video generation model that enables granularity controls in e.g., the scene appearance and the interactions between objects. I tried, and then realised the need of me inventing deep learning tricks for this goal is due to the issue of non-identifiability in my sequential deep generative models. In this talk I will discuss our research towards developing identifiable deep generative models in sequence modelling, and share some recent works regarding switching dynamic models. Throughout the talk I will highlight the balance between causality "Theorist" and deep learning "Alchemist", and discuss my opinions on the future of causal deep generative modelling research.