Zhi Zhou

Computer Vision and Pattern Recognition · International Workshop on Quality of Service · 2018–2020

A researcher in the Science corpus: 7 papers; trusts 122 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 93 citations into it; #8,784 of 21,575 by trust here, #7,092 by citations
• Artificial Intelligence — 91 citations into it
• Computer Networks and Communications — 62 citations into it; #2,600 of 9,886 by trust here, #1,192 by citations
• Efficiency & Compression — 42 citations into it
• Electrical and Electronic Engineering — 42 citations into it
• Federated & Distributed Learning — 22 citations into it
• Optimization & Training — 22 citations into it
• Model Architectures — 21 citations into it

Whom Zhi Zhou trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Song Han
4. Karen Simonyan
5. L. Eon Bottou
6. Ilya Sutskever
7. Yoshua Bengio
8. Yann LeCun
9. Christian Szegedy
10. Kaiming He

Who trusts Zhi Zhou overall

1. Jun Zhang
2. Khaled Ben Letaief
3. Xu Chen
4. En Li
5. Liekang Zeng
6. Patrick McEnroe
7. Yiwen Han
8. Md Maruf Hossain Shuvo
9. Wentai Wu
10. Guangming Shi

Papers from Zhi Zhou's perspective

Ordered by how strongly the researchers Zhi Zhou 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

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
• 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
• 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
• Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing (2018) — 435 citations