Chee-Kong Lee

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it
• Graph Learning — 14 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Chee-Kong Lee trusts

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

1. Martin Jaggi (in Optimization & Training)
2. Jure Leskovec (in Graph Learning)
3. William L. Hamilton (in Artificial Intelligence)
4. Vladlen Koltun (in Artificial Intelligence)

Papers from Chee-Kong Lee's perspective

Ordered by how strongly the researchers Chee-Kong Lee trusts endorse each paper. Same corpus, a different order for every researcher.

1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Neural Message Passing for Quantum Chemistry (International Conference on Machine Learning)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. Inductive Representation Learning on Large Graphs (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Motif-based Graph Self-Supervised Learning for Molecular Property Prediction (2021) — 370 citations