Workshop 32: Deep learning algorithms, theories and applications
Title: Advanced deep learning: algorithms and applications
Keywords: Deep learning, generalization theory, nonconvex optimization, internet of things, intelligent computing
Deep learning has achieved great success in many applications including but not limited to computer vision, speech recognition, and internet of things in the past decade. However, there remains a significant gap between the success of deep learning in practice and our ability to understand it at a fundamental mathematical level. This workshop focuses on the recent developments of deep learning algorithms, theories, as well as its applications, in particular in the fields of internet of things, communication and intelligent technology.
Chair: Prof. Jinshan Zeng |
Jiangxi Normal University
Jinshan Zeng is currently a distinguished professor with the School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, China, and serves as the director of the department of data science and big data. He is the principal investigator of two projects granted from the National Natural Science Foundation of China, and an awardee of “1000 Talents Plan”of Jiangxi Province. He has published about 50 papers in high-impact journals and conferences such as IEEE TPAMI, JMLR, IEEE TKDE, IEEE TSP, ICML, AAAI etc. He has had two papers co-authored with collaborators that received the International Consortium of Chinese Mathematicians (ICCM) Best Paper Award. He currently serves as an associate editor of the journal Frontiers in Applied Mathematics and Statistics. His research interests include deep learning, nonconvex optimization, computer vision, and remote sensing.