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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Book ChapterDOI
Introduction to Machine Learning
F. Richard Yu,Ying He +1 more
TL;DR: Machine learning is evolved from a collection of powerful techniques in AI areas and has been extensively used in data mining, which allows the system to learn the useful structural patterns and models from training data as discussed by the authors.
Journal ArticleDOI
Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach
Ying He,Nan Zhao,Hongxi Yin +2 more
TL;DR: This paper proposes an integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of next generation vehicular networks and formulate the resource allocation strategy in this framework as a joint optimization problem.
Journal ArticleDOI
Software-Defined Networks with Mobile Edge Computing and Caching for Smart Cities: A Big Data Deep Reinforcement Learning Approach
TL;DR: An integrated framework that can enable dynamic orchestration of networking, caching, and computing resources to improve the performance of applications for smart cities is proposed and a novel big data deep reinforcement learning approach is presented.
Journal ArticleDOI
Deep-Reinforcement-Learning-Based Optimization for Cache-Enabled Opportunistic Interference Alignment Wireless Networks
TL;DR: Simulation results are presented to show that the performance of cache-enabled opportunistic IA networks in terms of the network's sum rate and energy efficiency can be significantly improved by using the proposed approach.
Journal ArticleDOI
Fog Vehicular Computing: Augmentation of Fog Computing Using Vehicular Cloud Computing
Mehdi Sookhak,F. Richard Yu,Ying He,Hamid Talebian,Nader Sohrabi Safa,Nan Zhao,Muhammad Khurram Khan,Neeraj Kumar +7 more
TL;DR: This article proposes a new concept called fog vehicular computing (FVC) to augment the computation and storage power of fog computing and designs a comprehensive architecture for FVC and presents a number of salient applications.