【学术报告】How Does Data Freshness Affect Real-time Supervised Learning?

发布时间:2022-09-19 

报告题目:How Does Data Freshness Affect Real-time Supervised Learning?

报告人:美国Auburn大学 孙引教授

时间:2022年9月21日(周三),上午10:00-11:00

地点:腾讯会议 591-168-526

联系人:徐跃东 老师

 

摘要:The evolution of Artificial Intelligence and Internet technologies has engendered many networked intelligent systems, such as autonomous driving, remote surgery, video analytics, and factory automation. Real-time supervised learning is a crucial technique in these applications, where a neural network is trained to infer a time-varying target (e.g., the position of the vehicle in front) based on features (e.g., video frames) observed at a sensing node (e.g., camera or lidar). Due to the communication delay among network nodes, the features delivered to the neural network may not be fresh, impacting both the accuracy of real-time inference and the performance of networked intelligent systems. In this talk, we will discuss (i) how data freshness affects the performance of real-time supervised learning and (ii) how to design scheduling strategies to minimize inference error. The former problem is addressed using an information-theoretic analysis, with illustrations from several experiments. To solve the second problem, we explored a new connection between the Gittins index theory and Age of Information (AoI) minimization that was discovered recently. These results lay out a potential path toward contextual and goal-oriented status updating.

 

简历:Yin Sun is an Assistant Professor with the Department of Electrical and Computer Engineering, Auburn University, Alabama. He received the B.Eng. and Ph.D. degrees in Electronic Engineering from Tsinghua University, in 2006 and 2011, respectively. He was a Postdoctoral Scholar and Research Associate at the Ohio State University from 2011-2017. His research interests include Wireless Networks, Machine Learning, Robotic Control, and Information Freshness. He is a Guest Editor of the IEEE Journal on Selected Areas in Communications for the special issue on "Age of Information in Real-time Systems and Networks," a Guest Editor of Entropy for the special issue on "Age of Information: Concept, Metric and Tool for Network Control," an Associate Editor of the IEEE Transactions on Network Science and Engineering, and an Editor of the Journal of Communications and Networks. He has served in the organizing committees of several major conferences, including ACM MobiHoc 2019, 2021-2022, IEEE INFOCOM 2020-2021, IEEE/IFIP WiOpt 2020, IEEE WCNC 2021, and International Teletraffic Congress 2022 (ITC 34). He is a senior member of the IEEE and a member of the ACM. He founded the Age of Information Workshop in 2018. His articles received the Best Student Paper Award of the IEEE/IFIP WiOpt 2013, Best Paper Award of the IEEE/IFIP WiOpt 2019, runner-up for the Best Paper Award of ACM MobiHoc 2020, and 2021 Journal of Communications and Networks (JCN) Best Paper Award. He co-authored a monograph Age of Information: A New Metric for Information Freshness, published by Morgan & Claypool Publishers in 2019. He received the Auburn Author Award of 2020. His research group has maintained an online Paper Repository on Age of Information since 2016.