Xiaorui Liu

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 3 papers; trusts 103 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 119 citations into it; #26,284 of 35,345 by trust here, #12,387 by citations
• Natural Language Processing — 65 citations into it; #5,006 of 5,688 by trust here, #3,282 by citations
• Graph Learning — 34 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Statistical and Nonlinear Physics — 12 citations into it
• Reinforcement Learning — 10 citations into it
• Generative Models — 9 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Xiaorui Liu trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Oriol Vinyals
5. Ilya Sutskever
6. Alex Graves
7. Alex Krizhevsky
8. Yann LeCun
9. Max Welling
10. Tomas Mikolov

Who trusts Xiaorui Liu overall

1. Kun Qian
2. Marinka Zitnik

Papers from Xiaorui Liu's perspective

Ordered by how strongly the researchers Xiaorui Liu 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• A Survey on Dialogue Systems: Recent Advances and New Frontiers (2017) — 774 citations
• Is Homophily a Necessity for Graph Neural Networks? (2021) — 314 citations
• Graph Structure Learning for Robust Graph Neural Networks (2020) — 921 citations