Guolin Ke

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 9 papers; trusts 265 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 181 citations into it; #27,500 of 35,345 by trust here, #11,490 by citations
• Computer Vision and Pattern Recognition — 99 citations into it
• Representation & Self-Supervised Learning — 67 citations into it
• Natural Language Processing — 65 citations into it; #4,547 of 5,688 by trust here, #3,282 by citations
• Computational Theory and Mathematics — 58 citations into it
• Materials Chemistry — 49 citations into it
• Molecular Biology — 48 citations into it
• Model Architectures — 47 citations into it

Whom Guolin Ke trusts overall

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

1. Geoffrey E. Hinton
2. Jerome H. Friedman
3. Yoshua Bengio
4. Ilya Sutskever
5. Diederik P. Kingma
6. Alex Krizhevsky
7. Kaiming He
8. Yann LeCun
9. Ashish Vaswani
10. Jimmy Ba

Who trusts Guolin Ke overall

1. Yu Meng
2. Chengxuan Ying
3. Alexander Rakhlin
4. Ofir Press
5. Qinbin Li
6. Ibomoiye Domor Mienye
7. Ravid Shwartz-Ziv
8. Luke Merrick
9. Rajiv Punmiya
10. H. S. Anderson

Papers from Guolin Ke's perspective

Ordered by how strongly the researchers Guolin Ke 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. 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Rethinking Positional Encoding in Language Pre-training (2020) — 366 citations
• Do Transformers Really Perform Bad for Graph Representation? (2021) — 597 citations
• Uni-Mol: A Universal 3D Molecular Representation Learning Framework (2023) — 625 citations
• Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking? (2023) — 47 citations
• Do Transformers Really Perform Badly for Graph Representation? (2021) — 1,491 citations
• Variance-reduced Language Pretraining via a Mask Proposal Network (2020) — 8 citations
• Taking Notes on the Fly Helps BERT Pre-training (2020) — 10 citations
• Deep Subdomain Adaptation Network for Image Classification (2020) — 1,091 citations
• LightGBM: A Highly Efficient Gradient Boosting Decision Tree (2017) — 15,550 citations