Chaoyang He

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 12 papers; trusts 455 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 818 citations into it; #13,878 of 35,345 by trust here, #2,620 by citations
• Federated & Distributed Learning — 270 citations into it; #137 of 319 by trust here, #23 by citations
• Privacy & Security — 243 citations into it
• Optimization & Training — 235 citations into it
• Computer Vision and Pattern Recognition — 184 citations into it; #13,333 of 21,575 by trust here, #12,992 by citations
• Safety, Robustness & Interpretability — 115 citations into it
• Computer Networks and Communications — 113 citations into it
• Efficiency & Compression — 112 citations into it; #747 of 1,264 by trust here, #950 by citations

Whom Chaoyang He trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yoshua Bengio
6. Yann LeCun
7. Jimmy Ba
8. Diederik P. Kingma
9. H. Brendan McMahan
10. Ilya Sutskever

Who trusts Chaoyang He overall

1. Murali Annavaram
2. Jinhyun So
3. Amir Salman Avestimehr
4. Xinle Liang
5. Qinbin Li
6. S. Avestimehr
7. Songze Li
8. Ji Liu
9. Yang Liu
10. Qiong Wu

Papers from Chaoyang He's perspective

Ordered by how strongly the researchers Chaoyang He 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation (2020) — 146 citations
• Central Server Free Federated Learning over Single-sided Trust Social Networks (2019) — 80 citations
• PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers (2021) — 38 citations
• A Field Guide to Federated Optimization (2021) — 487 citations
• FairFed: Enabling Group Fairness in Federated Learning (2021) — 324 citations
• FedNAS: Federated Deep Learning via Neural Architecture Search (2020) — 98 citations
• Towards Non-I.I.D. and Invisible Data with FedNAS: Federated Deep Learning via Neural Architecture Search. (2020) — 30 citations
• Group Knowledge Transfer: Collaborative Training of Large CNNs on the Edge (2020) — 24 citations
• FedML: A Research Library and Benchmark for Federated Machine Learning (2020) — 716 citations
• Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge (2020) — 580 citations
• ADMSv2: A Modern Architecture for Transportation Data Management and Analysis (2019) — 13 citations