Mingzhe Chen

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 10 papers; trusts 147 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 262 citations into it; #11,782 of 35,345 by trust here, #5,600 by citations
• Electrical and Electronic Engineering — 179 citations into it; #1,090 of 16,539 by trust here, #2,240 by citations
• Computer Networks and Communications — 156 citations into it; #3,387 of 9,886 by trust here, #973 by citations
• Optimization & Training — 83 citations into it
• Computer Vision and Pattern Recognition — 68 citations into it
• Federated & Distributed Learning — 67 citations into it
• Efficiency & Compression — 42 citations into it
• ML Systems & Hardware — 39 citations into it

Whom Mingzhe Chen trusts overall

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

1. Ronald J. Williams
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Walid Saad
6. Kaiming He
7. J. Urgen Schmidhuber
8. Yann LeCun
9. Medhi Bennis
10. Yoshua Bengio

Who trusts Mingzhe Chen overall

1. Walid Saad
2. Zhaohui Yang
3. Jihong Park
4. Medhi Bennis
5. Khaled Ben Letaief
6. Ursula Challita
7. Yong Zeng
8. Yuanwei Liu
9. Yuanming Shi
10. Sumudu Samarakoon

Papers from Mingzhe Chen's perspective

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

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

Their papers

• A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems (2019) — 4,291 citations
• A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks (2019) — 1,442 citations
• Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017) — 871 citations
• Energy Efficient Federated Learning Over Wireless Communication Networks (2019) — 823 citations
• Convergence Time Optimization for Federated Learning Over Wireless Networks (2020) — 340 citations
• Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017) — 226 citations
• Communication-efficient federated learning (2021) — 358 citations
• Federated Learning for 6G: Applications, Challenges, and Opportunities (2021) — 344 citations
• Distributed Learning in Wireless Networks: Recent Progress and Future Challenges (2021) — 541 citations
• Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience (2016) — 671 citations