Generalization With Deep Learning: For Improvement On Sensing Capability

Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities.

In this edited volume, we aim to narrow the gap between humans and machines by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data.

Zhenghua Chen received his B.Eng. degree from University of Electronic Science and Technology of China in 2011, and PhD degree from NTU in 2017. After graduation, he worked as a research fellow in NTU until May 2018. Currently, he is a scientist at Institute for Infocomm Research, A*STAR Singapore. His research interests include data analytics for sensing, machine learning, deep learning, and transfer learning.

Wu Min received his B.Eng. degree in Computer Science from University of Science and Technology of China in 2006 and PhD degree in Computer Science from NTU in 2011. He is currently a Senior Scientist at the Data Analytics Department, Institute for Infocomm Research, A*STAR Singapore. His research interests include sensor data analytics, graph mining, machine learning, and bioinformatics.

Dr Li Xiaoli is Head of Machine Intellection Department and a principal scientist at Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR) Singapore. He also holds adjunct position at School of Computer Science and Engineering, Nanyang Technological University (NTU). His research interests include data mining, machine learning, AI, and bioinformatics. He has served as (senior) PC member/workshop chair/session chair in leading data mining and AI related conferences, such as International Joint Conference on Artificial Intelligence (IJCAI) and International Conference on Data Mining (ICDM) to name a few. He has published more than 190 peer-reviewed papers, in top tier journals such as IEEE Transactions TKDE, IEEE Transactions on Reliability, and Bioinformatics. His areas of specialisation are positive unlabelled based learning and social/biological network mining. His paper ""Drug-target interaction prediction via class imbalance-aware ensemble learning"" won the Best Paper Award at the International Conference on Bioinformatics 2016. He has also won Best Performance Awards at the Opportunity Activity Recognition Challenge conducted by the EU Consortium and 2nd Dialogue for Reserve Engineering Assessments and Methods (DREAM 2007), respectively. With rich translational experience in working with industry, Dr Li has led over 10 R&D projects in collaboration with industry partners across sectors, including leading aerospace companies, banks, telecom companies, and insurance companies.