Y
Ying He
Researcher at Carleton University
Publications - 46
Citations - 4330
Ying He is an academic researcher from Carleton University. The author has contributed to research in topics: Reinforcement learning & Computer science. The author has an hindex of 20, co-authored 35 publications receiving 3431 citations. Previous affiliations of Ying He include Dalian University of Technology & Shenzhen University.
Papers
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Journal ArticleDOI
RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models
TL;DR: In this article , a pre-trained diffusion model is used to guide the NeRF model to generate new 3D objects, which can improve the editability, diversity, and application range of NeRF.
Proceedings ArticleDOI
When Multi-access Edge Computing Meets Multi-area Intelligent Reflecting Surface: A Multi-agent Reinforcement Learning Approach
TL;DR: An efficient resource provisioning scheme for multi-IRS multi-area scenarios in MEC networks using a multi-agent actor-critic method with an attention mechanism for resource management with latency guarantee is proposed.
Book ChapterDOI
Deep Reinforcement Learning for Interference Alignment Wireless Networks
F. Richard Yu,Ying He +1 more
TL;DR: This chapter proposes a novel deep reinforcement learning approach, which is an advanced reinforcement learning algorithm that uses deep Q network to approximate the Q value-action function and uses Google TensorFlow to implement this approach to obtain the optimal IA user selection policy in cache-enabled opportunistic IA wireless networks.
Journal ArticleDOI
An HTTP Anomaly Detection Architecture Based on the Internet of Intelligence
TL;DR: A new anomaly detection architecture based on the concept of the “Internet of intelligence” is designed that can be applied to different IoT anomaly detection methods and can enhance the detection performance of abnormal HTTP traffic in IoT and address the challenges of existing approaches.
Proceedings ArticleDOI
$Q_{C}-DQN$: A Novel Constrained Reinforcement Learning Method for Computation Offloading in Multi-access Edge Computing
TL;DR: This article presents a novel framework for MEC networks with unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) to facilitate computation offloading with delay constraints and proposes a novel constrained reinforcement learning method with a dynamic balance mechanism named $Q_{c}-DQN$.