Keyulu Xu

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 104 researchers and is trusted by 105 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 126 citations into it; #2,441 of 35,345 by trust here, #2,131 by citations
• Graph Learning — 61 citations into it; #48 of 693 by trust here, #43 by citations
• Computer Vision and Pattern Recognition — 52 citations into it
• Model Architectures — 29 citations into it
• Representation & Self-Supervised Learning — 24 citations into it; #1,648 of 3,145 by trust here, #907 by citations
• Optimization & Training — 22 citations into it
• Scaling & Learning Theory — 22 citations into it
• Statistical and Nonlinear Physics — 19 citations into it; #257 of 10,127 by trust here, #114 by citations

Whom Keyulu Xu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Kaiming He
6. Simon Shaolei Du
7. Yann LeCun
8. Jimmy Ba
9. Ilya Sutskever
10. Max Welling

Who trusts Keyulu Xu overall

1. Matthias Fey
2. Vijay Prakash Dwivedi
3. William L. Hamilton
4. Weihua Hu
5. Christopher Morris
6. Jure Leskovec
7. Guohao Li
8. Johannes Klicpera
9. Jing Huang
10. Jiaxuan You

Papers from Keyulu Xu's perspective

Ordered by how strongly the researchers Keyulu Xu 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• How Powerful are Graph Neural Networks? (2018) — 10,110 citations
• Representation Learning on Graphs with Jumping Knowledge Networks (2018) — 2,401 citations
• How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks (2020) — 370 citations
• Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (2019) — 311 citations