Kai Yang

Artificial Intelligence · with Jiang · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 82 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 112 citations into it
• Electrical and Electronic Engineering — 106 citations into it; #2,553 of 16,539 by trust here, #5,458 by citations
• Computer Networks and Communications — 60 citations into it
• Computer Vision and Pattern Recognition — 51 citations into it
• Optimization & Training — 50 citations into it
• Efficiency & Compression — 39 citations into it
• Computational Mechanics — 34 citations into it; #3,024 of 10,001 by trust here, #3,029 by citations
• Federated & Distributed Learning — 17 citations into it

Whom Kai Yang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Song Han
4. Yann LeCun
5. Bobak Anthony Nazer
6. Yoshua Bengio
7. Yurii E. Nesterov
8. Kaiming He
9. H. Brendan McMahan
10. Diederik P. Kingma

Who trusts Kai Yang overall

1. Mohammad Mohammadi Amiri
2. Guangxu Zhu
3. Howard H. Yang
4. Deniz Gündüz
5. Zhaohui Yang
6. Yuanming Shi
7. Khaled Ben Letaief
8. Mingzhe Chen
9. Tomer Sery
10. Wenqi Shi

Papers from Kai Yang's perspective

Ordered by how strongly the researchers Kai Yang 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Millimeter Wave Communications for Future Mobile Networks (2017) — 1,023 citations
• Federated Learning via Over-the-Air Computation (2018) — 1,082 citations
• Communication-Efficient Edge AI: Algorithms and Systems (2020) — 441 citations