Changchuan Yin

Electrical and Electronic Engineering · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 71 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 109 citations into it; #5,031 of 16,539 by trust here, #4,283 by citations
• Artificial Intelligence — 108 citations into it; #16,912 of 35,345 by trust here, #8,077 by citations
• Computer Networks and Communications — 97 citations into it; #3,116 of 9,886 by trust here, #973 by citations
• Computer Vision and Pattern Recognition — 38 citations into it
• Optimization & Training — 29 citations into it
• Aerospace Engineering — 26 citations into it; #3,707 of 6,421 by trust here, #2,245 by citations
• Model Architectures — 20 citations into it
• Signal Processing — 17 citations into it

Whom Changchuan Yin trusts overall

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

1. Ronald J. Williams
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Paul J. Werbos
5. Diederik P. Kingma
6. Herbert Jaeger
7. Alex Krizhevsky
8. Richard S. Sutton
9. Yann LeCun
10. Kaiming He

Who trusts Changchuan Yin overall

1. Mingzhe Chen
2. Walid Saad
3. Zhaohui Yang
4. Jihong Park
5. Ursula Challita

Papers from Changchuan Yin's perspective

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

Their papers

• 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
• Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017) — 226 citations
• Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience (2016) — 671 citations