Hao Ye

Artificial Intelligence · IEEE Wireless Communications Letters · 2017–2019

A researcher in the Science corpus: 7 papers; trusts 38 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #6,664 of 35,345 by trust here, #9,170 by citations
• Electrical and Electronic Engineering — 27 citations into it; #1,255 of 16,539 by trust here, #5,574 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #21,012 of 21,575 by trust here, #16,787 by citations
• Reinforcement Learning — 14 citations into it
• Computer Networks and Communications — 13 citations into it; #6,194 of 9,886 by trust here, #7,492 by citations
• Aerospace Engineering — 10 citations into it; #3,186 of 6,421 by trust here, #3,175 by citations
• Generative Models — 9 citations into it
• Molecular Biology — 7 citations into it; #16,897 of 17,773 by trust here, #14,509 by citations

Whom Hao Ye trusts overall

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

1. Diederik P. Kingma
2. Geoffrey Y. Li
3. Jimmy Ba
4. Geoffrey E. Hinton
5. Tim O'Shea
6. Nariman Farsad
7. Alex Krizhevsky
8. Eliya Nachmani
9. Yoshua Bengio
10. Volodymyr Mnih

Who trusts Hao Ye overall

1. Geoffrey Y. Li
2. Woongsup Lee
3. Hengtao He
4. Zhijin Qin
5. Le Liang
6. Fengxiao Tang
7. Alessio Zappone
8. Chao-Kai Wen
9. Shi Jin
10. S. Nawaz

Papers from Hao Ye's perspective

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

Their papers

• Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems (2017) — 1,736 citations
• Deep Reinforcement Learning Based Resource Allocation for V2V Communications (2018) — 722 citations
• Machine Learning for Vehicular Networks: Recent Advances and Application Examples (2017) — 266 citations
• Spectrum Sharing in Vehicular Networks Based on Multi-Agent Reinforcement Learning (2019) — 444 citations
• Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN (2018) — 234 citations
• Deep Learning-Based End-to-End Wireless Communication Systems With Conditional GANs as Unknown Channels (2019) — 353 citations
• Deep Learning in Physical Layer Communications (2018) — 641 citations