1 citations · 3 across the 7 of their papers we have counts for
7 papers
Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach
Wenhan Yu, Terence Jie Chua, Jun Zhao
The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm integrating M…
Heterogeneous 360 Degree Videos in Metaverse: Differentiated Reinforcement Learning Approaches
Wenhan Yu, Jun Zhao
Advanced video technologies are driving the development of the futuristic Metaverse, which aims to connect users from anywhere and anytime. As such, the use cases for users will be…
Semantic communications, semantic edge computing, and semantic caching
Wenhan Yu, Jun Zhao
The increasing popularity of applications like the Metaverse has led to the exploration of new, more effective ways of communication. Semantic communication, which focuses on the m…
Detection of Uncertainty in Exceedance of Threshold (DUET): An Adversarial Patch Localizer
Terence Jie Chua, Wenhan Yu, Jun Zhao
Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patc…
Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning
Terence Jie Chua, Wenhan Yu, Jun Zhao
Real-time Digital Twinning of physical world scenes onto the Metaverse is necessary for a myriad of applications such as augmented-reality (AR) assisted driving. In AR assisted dri…
Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning
Wenhan Yu, Terence Jie Chua, Jun Zhao
The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the…