Keynote 1 by Dacheng Tao
AI – Quest for Deep Learning
by: Dacheng Tao (University of Sydney)
Abstract — We are fortunate on the edge to embrace the revolutionary progress of Artificial Intelligence (AI) and to witness the enthusiasm of translational AI deployments sweeping across all sectors in our life. The almost simultaneous rise of transformational deep learning, big data, and powerful computational machines since 2010 is progressively enabling AI systems to perceive, learn, reason, and behave like a human, and makes the next generation of AI systems distinct from those developed in the past. Thus, it is critical to better understand the role of deep learning in AI. In this talk, we will present our investigations, initiatives, and insights to the interpretation of the successful deep learning, such as why is deep structure superior to shallow structure, how do skip connections affect model’s performance, and what is the relationship between some of the free parameters.
Speaker’s Biography — Dacheng Tao (F’15) is Professor of Computer Science and ARC Laureate Fellow in the School of Computer Science and the Faculty of Engineering, and the Inaugural Director of the UBTECH Sydney Artificial Intelligence Centre, at The University of Sydney. His research results in artificial intelligence have expounded in one monograph and 200+ publications at prestigious journals and prominent conferences, such as IEEE T-PAMI, IJCV, JMLR, AIJ, AAAI, IJCAI, NIPS, ICML, CVPR, ICCV, ECCV, ICDM, and KDD, with several best paper awards. He received the 2018 IEEE ICDM Research Contributions Award and the 2015 Australian Scopus-Eureka prize. He is a Fellow of the IEEE, AAAS, ACM and the Australian Academy of Science.