Yan Kang

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 7 papers; trusts 25 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 297 citations into it; #15,695 of 35,345 by trust here, #6,229 by citations
• Federated & Distributed Learning — 93 citations into it; #240 of 319 by trust here, #71 by citations
• Privacy & Security — 87 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Information Systems — 34 citations into it
• Electrical and Electronic Engineering — 29 citations into it
• Safety, Robustness & Interpretability — 29 citations into it
• Computational Theory and Mathematics — 25 citations into it

Whom Yan Kang trusts overall

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

1. Yang Liu
2. H. Brendan McMahan
3. Tat-seng Chua
4. Cynthia Dwork
5. Alex Krizhevsky
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Yann LeCun
9. Diederik P. Kingma
10. Kaiming He

Who trusts Yan Kang overall

1. Yang Liu
2. Xuefei Yin
3. Qinbin Li
4. Siwei Feng
5. Han Yu

Papers from Yan Kang's perspective

Ordered by how strongly the researchers Yan Kang 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Federated Learning (2019) — 1,040 citations
• FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features (2022) — 139 citations
• Vertical Federated Learning: Concepts, Advances, and Challenges (2022) — 384 citations
• FedML: A Research Library and Benchmark for Federated Machine Learning (2020) — 716 citations
• A Secure Federated Transfer Learning Framework (2020) — 549 citations
• A Communication Efficient Vertical Federated Learning Framework (2019) — 79 citations
• Secure and Efficient Federated Transfer Learning (2019) — 96 citations