Chengsheng Mao

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 84 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #9,175 of 35,345 by trust here, #11,192 by citations
• Natural Language Processing — 35 citations into it; #2,277 of 5,688 by trust here, #2,955 by citations
• Graph Learning — 26 citations into it; #270 of 693 by trust here, #335 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Molecular Biology — 11 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• Management Science and Operations Research — 7 citations into it
• Model Architectures — 6 citations into it

Whom Chengsheng Mao trusts overall

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

1. Richard Socher
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yuan Luo
5. Tomas Mikolov
6. Ilya Sutskever
7. Diederik P. Kingma
8. Jeffrey Pennington
9. Yann LeCun
10. Christopher D. Manning

Who trusts Chengsheng Mao overall

1. Yangjun Zhang
2. Shirui Pan
3. Haopeng Zhang
4. Hao Zhu
5. Feng Xia
6. Shervin Minaee
7. Giannis Nikolentzos
8. Qi Liu
9. Chen Zhang
10. Ciyuan Peng

Papers from Chengsheng Mao's perspective

Ordered by how strongly the researchers Chengsheng Mao 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Graph Convolutional Networks for Text Classification (2018) — 2,101 citations
• KG-BERT: BERT for Knowledge Graph Completion (2019) — 821 citations