Topic: Beyond Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models (GDMs) in Network Optimization
Speaker: Professor Dusit Niyato, Nanyang Technological University (NTU)
Date and time: April 29, 10:00–11:00
Venue: Room 1A-200, SIST
Host: Shi Yuanming
Abstract:
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a variety of applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This presentation gives an introduction on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains. The presentation first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), Semantic Communications (SemCom), and Internet of Vehicles (IoV) networks. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design.
Biography:
Dusit Niyato is currently a President’s Chair Professor in Computer Science and Engineering in the College of Computing & Data Science (CCDS), Nanyang Technological University, Singapore. He received his BE degree from King Mongkuk’s Institute of Technology Ladkrabang (KMITL), Thailand, in 1999 and PhD in Electrical and Computer Engineering from the University of Manitoba, Canada in 2008. Dusit’s research interests are in the areas of distributed collaborative machine learning, Internet of Things (IoT), edge intelligent generative AI and AI-generated content (AIGC), mobile and distributed computing, and wireless networks. Dusit won the IEEE Communications Society (ComSoc) Best Survey Paper Award, IEEE Asia-Pacific Board (APB) Outstanding Paper Award, the IEEE Computer Society Middle Career Researcher Award for Excellence in Scalable Computing and Distinguished Technical Achievement Recognition Award of IEEE ComSoc Technical Committee on Green Communications and Computing. Currently, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials (impact factor of 35.6 for 2023), an area editor of IEEE Transactions on Vehicular Technology, editor of IEEE Transactions on Wireless Communications, associate editor of IEEE Internet of Things Journal, IEEE Transactions on Mobile Computing, IEEE Wireless Communications, IEEE Network, IEEE Transactions on Information Forensics and Security (TIFS), and ACM Computing Surveys. He was a guest editor of IEEE Journal on Selected Areas in Communications. He is the Members-at-Large to the Board of Governors of IEEE Communications Society for 2024–2026, and was a Distinguished Lecturer of the IEEE Communications Society for 2016–2017. He was named the 2017–2023 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET.