Bomin Mao

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 32 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #13,941 of 35,345 by trust here, #6,911 by citations
• Computer Vision and Pattern Recognition — 33 citations into it
• Computer Networks and Communications — 30 citations into it; #3,138 of 9,886 by trust here, #843 by citations
• Signal Processing — 22 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Natural Language Processing — 13 citations into it
• Model Architectures — 10 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Bomin Mao trusts overall

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

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

Who trusts Bomin Mao overall

1. Nei Kato
2. Fengxiao Tang
3. Zubair Muhammad Fadlullah
4. Qian Mao
5. Chaoyun Zhang
6. Hongji Huang
7. Guan Gui
8. Jiajia Liu
9. Muhammad Usama
10. Yuichi Kawamoto

Papers from Bomin Mao's perspective

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

Their papers

• Ten Challenges in Advancing Machine Learning Technologies toward 6G (2020) — 347 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