报告题目：Sensor-Based Activity Recognition via Kernel-embedding Neural Networks
报 告 人：Sinno Jialin Pan (新加坡南洋理工大学 副教授)
会议地点：线上，腾讯会议号: 380 645 135
报告摘要：Feature-engineering-based machine learning models and deep learning models have been explored for wearable-sensor-based human activity recognition. For both types of methods, one crucial research issue is how to extract proper features from the partitioned segments of multivariate sensor readings. Existing methods have different drawbacks: 1) feature-engineering-based methods are able to extract meaningful features, such as statistical or structural information underlying the segments, but usually require manual designs of features for different applications, which is time consuming, and 2) deep learning models are able to learn temporal and/or spatial features from the sensor data automatically, but fail to capture statistical information. In this talk, I will introduce our recently developed kernel-embedding neural architecture that is able to automatically learn meaningful features including statistical features, temporal features and spatial correlation features for activity recognition. I will also discuss some advanced issues in sensor-based activity recognition.
报告人简介：Sinno Jialin Pan is a Provost’s Chair Associate Professor with the School of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He received his Ph.D. degree in computer science from the Hong Kong University of Science and Technology (HKUST) in 2011. Prior to joining NTU, he was a scientist and Lab Head of text analytics with the Data Analytics Department at Institute for Infocomm Research, Singapore. He joined NTU as a Nanyang Assistant Professor in 2014. He was named to the list of “AI 10 to Watch” by the IEEE Intelligent Systems magazine in 2018. He serves as an Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Artificial Intelligence (AIJ) and ACM Transactions on Intelligent Systems and Technology (TIST). His research interests include transfer learning and its real-world applications.