Han Yu

Artificial Intelligence · IEEE Transactions on Neural Networks and Learning Systems · 2010–2021

A researcher in the Science corpus: 14 papers; trusts 676 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 850 citations into it; #9,432 of 35,345 by trust here, #2,066 by citations
• Privacy & Security — 305 citations into it; #691 of 709 by trust here, #570 by citations
• Federated & Distributed Learning — 227 citations into it; #254 of 319 by trust here, #23 by citations
• Computer Vision and Pattern Recognition — 185 citations into it; #20,362 of 21,575 by trust here, #16,367 by citations
• Safety, Robustness & Interpretability — 159 citations into it
• Optimization & Training — 128 citations into it
• Computer Networks and Communications — 105 citations into it
• Information Systems — 72 citations into it; #6,364 of 8,771 by trust here, #3,198 by citations

Whom Han Yu trusts overall

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

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

Who trusts Han Yu overall

1. Lichao Sun
2. Mingxia Liu
3. Lingjuan Lyu
4. Haftay Gebreslasie Abreha
5. Sicong Che
6. Jindong Wang
7. Priyanka Mary Mammen
8. Xuefei Yin
9. Liangqi Yuan
10. Jihong Park

Papers from Han Yu's perspective

Ordered by how strongly the researchers Han Yu 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Visual Domain Adaptation with Manifold Embedded Distribution Alignment (2018) — 651 citations
• Advances and Open Problems in Federated Learning (2019) — 9,019 citations
• A Survey of Zero-Shot Learning (2019) — 777 citations
• Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach (2019) — 249 citations
• Federated Learning (2019) — 1,040 citations
• Towards Personalized Federated Learning (2021) — 1,295 citations
• FedCoin: A Peer-to-Peer Payment System for Federated Learning (2020) — 133 citations
• A Sustainable Incentive Scheme for Federated Learning (2020) — 119 citations
• Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention (2020) — 39 citations
• Privacy and Robustness in Federated Learning: Attacks and Defenses (2020) — 538 citations
• Multi-Participant Multi-Class Vertical Federated Learning (2020) — 98 citations
• Threats to Federated Learning: A Survey (2020) — 545 citations
• Towards Fair and Privacy-Preserving Federated Deep Models (2019) — 244 citations
• A Survey of Trust and Reputation Management Systems in Wireless Communications (2010) — 323 citations