Liang Yao

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 99 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #8,472 of 35,345 by trust here, #10,798 by citations
• Natural Language Processing — 42 citations into it; #2,276 of 5,688 by trust here, #2,955 by citations
• Graph Learning — 34 citations into it; #269 of 693 by trust here, #335 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Molecular Biology — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Management Science and Operations Research — 8 citations into it
• Model Architectures — 6 citations into it

Whom Liang Yao trusts overall

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

1. Yoshua Bengio
2. Richard Socher
3. Geoffrey E. Hinton
4. Antoine Bordes
5. Tomas Mikolov
6. Ilya Sutskever
7. Diederik P. Kingma
8. Yuan Luo
9. Yann LeCun
10. Alex Krizhevsky

Who trusts Liang Yao overall

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

Papers from Liang Yao's perspective

Ordered by how strongly the researchers Liang Yao 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
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
• Incorporating Knowledge Graph Embeddings into Topic Modeling (2017) — 66 citations