| |
 Achieving Upper Bound Accuracy in Continual Learning Speaker (s):
Liu Bing
Distinguished Professor,
Department of Computer Science
University of Illinois at Chicago (UIC)
| Date: Time: Venue: | | 9 January 2025, Thursday 10:30am – 11:30am School of Computing &
Information Systems 1 (SCIS 1)
Level 4, Meeting Room 4-4
Singapore Management University
80 Stamford Road,
Singapore 178902 Please register by 8 January 2025. We look forward to seeing you at this research seminar. 
|
|
About the Talk The ability to continuously learn and accumulate knowledge over a lifetime is a hallmark of human intelligence. Yet, this critical capability is absent from current machine learning paradigms. This talk delves into continual learning, focusing on addressing the challenges of catastrophic forgetting and inter-task class separation—key obstacles that have kept existing methods from approaching their theoretical upper bound. Our recent theoretical and empirical results demonstrate that achieving this upper bound is indeed possible, offering insights into both cognitive processes and foundations of AI. About the Speaker Bing Liu is a Distinguished Professor and Peter L. and Deborah K. Wexler Professor of Computing at the University of Illinois Chicago. He earned his Ph.D. from the University of Edinburgh. His current research interests include continual or lifelong learning, continual learning dialogue systems, sentiment analysis, machine learning, and natural language processing. He is the author of several books on these topics and has also received multiple Test-of-Time awards for his research papers. Liu is the 2018 recipient of the ACM SIGKDD Innovation Award and a Fellow of ACM, AAAI, and IEEE. |
