Abstract：The classic machine learning paradigm learns in isolation, which is only suitable for well-defined narrow tasks in closed environments. It is far from sufficient for truly intelligent systems, which must learn continually, accumulate the knowledge learned in the past, and selectively transfer the knowledge to help learn each new task. The machine learning paradigm, continual/lifelong learning, aims to achieve these goals. In this talk, I will first introduce the context of and motivation for continual learning and then discuss some of our recent work on continual learning of natural language processing tasks.

Time：Sep 22, 2021 10:00 PM in Beijing, Shanghai

Bing Liu is a distinguished professor at the University of Illinois at Chicago. He received his Ph.D. in AI from the University of Edinburgh. His current research interests include continual/lifelong learning, sentiment analysis, dialogue systems, natural language processing (NLP), machine learning and data mining. He has published extensively in top conferences and journals and authored four books: one about lifelong machine learning, two about sentiment analysis, and one about Web mining. Three of his papers received Test-of-Time awards and another one received Test-of-Time honorable mention. Some of his works have also been widely reported in the popular press internationally. He served as the Chair of ACM SIGKDD from 2013-2017, as program chair of many leading data mining conferences, and as area/track chair or senior PC member of numerous NLP, AI, Web and data mining conferences. He is the winner of 2018 ACM SIGKDD Innovation Award, and is a Fellow of ACM, AAAI and IEEE.