Fengxiao Tang

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 8 papers; trusts 37 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #14,043 of 35,345 by trust here, #6,266 by citations
• Computer Networks and Communications — 45 citations into it; #3,206 of 9,886 by trust here, #843 by citations
• Electrical and Electronic Engineering — 37 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Signal Processing — 27 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Natural Language Processing — 13 citations into it
• Model Architectures — 10 citations into it

Whom Fengxiao Tang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Ilya Sutskever
5. Ruslan Salakhutdinov
6. Yann LeCun
7. Nei Kato
8. Radford M. Neal
9. Diederik P. Kingma
10. Richard Socher

Who trusts Fengxiao Tang overall

1. Nei Kato
2. Bomin Mao
3. Guan Gui
4. Hongji Huang
5. Zubair Muhammad Fadlullah
6. Mostafa Zaman Chowdhury
7. Jiajia Liu
8. Yuichi Kawamoto
9. Xiaofei Wang
10. Chaoyun Zhang

Papers from Fengxiao Tang's perspective

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

Their papers

• Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020) — 499 citations
• Ten Challenges in Advancing Machine Learning Technologies toward 6G (2020) — 347 citations
• 6G: Opening New Horizons for Integration of Comfort, Security, and Intelligence (2020) — 571 citations
• Optimizing Space-Air-Ground Integrated Networks by Artificial Intelligence (2018) — 353 citations
• On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control (2018) — 236 citations
• State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017) — 729 citations
• The Deep Learning Vision for Heterogeneous Network Traffic Control: Proposal, Challenges, and Future Perspective (2017) — 388 citations
• Routing or Computing? The Paradigm Shift Towards Intelligent Computer Network Packet Transmission Based on Deep Learning (2017) — 312 citations