Qiongkai Xu

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 73 researchers and is trusted by 66 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it; #3,065 of 35,345 by trust here, #5,638 by citations
• Representation & Self-Supervised Learning — 37 citations into it; #644 of 3,145 by trust here, #1,234 by citations
• Natural Language Processing — 35 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it; #5,628 of 21,575 by trust here, #7,499 by citations
• Safety, Robustness & Interpretability — 12 citations into it
• Graph Learning — 9 citations into it; #79 of 693 by trust here, #146 by citations
• Statistical and Nonlinear Physics — 9 citations into it; #1,350 of 10,127 by trust here, #1,638 by citations
• Evaluation & Benchmarks — 6 citations into it

Whom Qiongkai Xu trusts overall

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

1. Geoffrey E. Hinton
2. Lei Tang
3. Tomas Mikolov
4. John A. Bullinaria
5. Omer Levy
6. Ilya Sutskever
7. Yoshua Bengio
8. Richard Socher
9. Alex Krizhevsky
10. Diederik P. Kingma

Who trusts Qiongkai Xu overall

1. Bryan Perozzi
2. Daokun Zhang
3. William L. Hamilton
4. Shirui Pan
5. Aleksandar Bojchevski
6. Peng Cui
7. Hongyun Cai
8. Liyu Gong
9. Benedek Rozemberczki
10. Vincent Wenchen Zheng

Papers from Qiongkai Xu's perspective

Ordered by how strongly the researchers Qiongkai Xu 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. Attention is All you Need (Neural Information Processing Systems)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep Neural Networks for Learning Graph Representations (2016) — 1,030 citations
• Adhering, Steering, and Queering: Treatment of Gender in Natural Language Generation (2020) — 49 citations
• GraRep: Learning Graph Representations with Global Structural Information (2015) — 1,738 citations