Automated Machine Learning and Meta-Learning for Multimedia - Softcover

Zhu, Wenwu; Wang, Xin

Softcover

ISBN 10: 3030881342 ISBN 13: 9783030881344

Publisher: Springer, 2022

Synopsis

This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry.

This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.

About this title

About the Author

Dr. Wenwu Zhu is currently a Professor of Computer Science Department of Tsinghua University and Vice Dean of National Research Center on Information Science and Technology, Prior to his current post, he was a Senior Researcher and Research Manager at Microsoft Research Asia. He was the Chief Scientist and Director at Intel Research China from 2004 to 2008. He worked at Bell Labs New Jersey as a Member of Technical Staff during 1996-1999. 

He served as the Editor-in-Chief for the IEEE Transactions on Multimedia (T-MM) from January 1, 2017 to December 31, 2019. He has been serving as the chair of the steering committee for IEEE T-MM and Vice EiC for IEEE Transactions on Circuits and Systems for Video Technology (TCSVT) since January 1, 2020. His current research interests are in the areas of multimedia big data and intelligence, and multimedia networking including edge computing. He received nine Best Paper Awards. He is an IEEE Fellow, AAAS Fellow, SPIE Fellow, and a member ofAcademia Europaea.

Dr.Xin Wang is currently an Assistant Professor at the Department of Computer Science and Technology, Tsinghua University. He got both of his Ph.D. and B.E degrees in Computer Science and Technology from Zhejiang University, China. He also holds a Ph.D. degree in Computing Science from Simon Fraser University, Canada. His research interests include multimedia intelligence, big data analysis and machine learning. He has published several high-quality research papers in top journals and conferences including TPAMI, ICML, KDD, WWW, SIGIR ACM Multimedia etc. He is the recipient of 2017 China Postdoctoral innovative talents supporting program. He receives the ACM China Rising Star Award in 2020.

From the Back Cover

This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry.

This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.

Edition details

• Publisher: Springer
• Publication date: 2022
• Language: English
• ISBN 10: 3030881342
• ISBN 13: 9783030881344
• Binding: Paperback
• Edition number: 1
• Number of pages: 251