报告人： Yingzhen Li (Imperial College London）
地点：Room 1303, Sciences Building No. 1；Tencent Meeting(789-347-545)
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 and on-going 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.
About the Speaker:
Dr Yingzhen Li is a Lecturer (equiv. US assistant professor) in Machine Learning at Imperial College London, UK. Before that she worked at Microsoft Research Cambridge and Disney Research. She received her PhD from the University of Cambridge. Yingzhen is passionate about building reliable machine learning systems with probabilistic methods, and her published work has been applied in industrial systems and implemented in popular deep learning frameworks. She is a regularly invited speaker at international machine learning conferences and machine learning summer schools, and she gave an invited tutorial at NeurIPS 2020. Her work on Bayesian ML has also been recognised in AAAI 2023 New Faculty Highlights. She has co-organised many international research workshops on probabilistic inference and deep generative models. She regularly serves as Area Chair for ICML, ICLR and NeurIPS, and currently she is a Program Chair for AISTATS 2024. When not at work, Yingzhen enjoys reading, travel, video games, and following news on latest technology developments.
Tencent Meeting： https://meeting.tencent.com/dm/kpuFWrS212Ox
Your participation is warmly welcomed!