Xu Chen

Artificial Intelligence · IEEE Communications Surveys and Tutorials · 2014–2020

A researcher in the Science corpus: 11 papers; trusts 283 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 180 citations into it; #19,889 of 35,345 by trust here, #6,569 by citations
• Computer Networks and Communications — 148 citations into it; #358 of 9,886 by trust here, #426 by citations
• Computer Vision and Pattern Recognition — 143 citations into it; #7,135 of 21,575 by trust here, #5,680 by citations
• Electrical and Electronic Engineering — 95 citations into it
• Efficiency & Compression — 64 citations into it
• Federated & Distributed Learning — 48 citations into it
• Optimization & Training — 34 citations into it
• Model Architectures — 27 citations into it

Whom Xu Chen trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Tim Roughgarden
3. Geoffrey E. Hinton
4. Karthik Kumar
5. Yann LeCun
6. Yoshua Bengio
7. Ilya Sutskever
8. Karen Simonyan
9. Dov Monderer
10. L. Eon Bottou

Who trusts Xu Chen overall

1. Chen-meng Wang
2. Tuyen X. Tran
3. Cheol-Ho Hong
4. Yuyi Mao
5. Stefania Sardellitti
6. Jianbo Du
7. Xinchen Lyu
8. Jun Zhang
9. Yanting Wang
10. F. Yu

Papers from Xu Chen's perspective

Ordered by how strongly the researchers Xu Chen trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• CoEdge: Cooperative DNN Inference With Adaptive Workload Partitioning Over Heterogeneous Edge Devices (2020) — 311 citations
• HFEL: Joint Edge Association and Resource Allocation for Cost-Efficient Hierarchical Federated Edge Learning (2020) — 426 citations
• Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework (2020) — 348 citations
• FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring (2020) — 391 citations
• Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing (2019) — 870 citations
• Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019) — 1,229 citations
• Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing (2019) — 1,896 citations
• Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy (2018) — 392 citations
• In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2018) — 892 citations
• Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing (2018) — 435 citations
• Decentralized Computation Offloading Game for Mobile Cloud Computing (2014) — 852 citations