Xiaofei Wang
Computer Networks and Communications · IEEE Communications Surveys and Tutorials · 2011–2020
A researcher in the Science corpus: 7 papers; trusts 25 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Computer Networks and Communications — 110 citations into it; #9,830 of 9,886 by trust here, #891 by citations
- Artificial Intelligence — 106 citations into it
- Computer Vision and Pattern Recognition — 53 citations into it
- Federated & Distributed Learning — 28 citations into it
- Efficiency & Compression — 25 citations into it
- Reinforcement Learning — 15 citations into it
- Model Architectures — 7 citations into it
- Hardware and Architecture — 5 citations into it
Whom Xiaofei Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Hado van Hasselt
- Geoffrey E. Hinton
- Richard S. Sutton
- Edward A. Lee
- Yoshua Bengio
- Alex Krizhevsky
- Yann LeCun
- David Silver
- Song Han
- H. Brendan McMahan
Who trusts Xiaofei Wang overall
- Boyi Liu
- Dinh C. Nguyen
- Haftay Gebreslasie Abreha
- Jihong Park
- Manuel Eugenio Morocho-Cayamcela
- Alireza Sadeghi
- Nisha Panwar
- Mingzhe Chen
- Ming Ding
- Lujia Wang
Papers from Xiaofei Wang's perspective
Ordered by how strongly the researchers Xiaofei Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Going deeper with convolutions
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching (2020) — 364 citations
- Federated learning for 6G communications: Challenges, methods, and future directions (2020) — 331 citations
- A Survey of Green Mobile Networks: Opportunities and Challenges (2011) — 377 citations
- Hierarchical Edge Caching in Device-to-Device Aided Mobile Networks: Modeling, Optimization, and Design (2018) — 122 citations
- Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019) — 1,229 citations
- In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2018) — 892 citations
- Cache in the air: exploiting content caching and delivery techniques for 5G systems (2014) — 1,045 citations