Wenqi Fan

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 4 papers; trusts 21 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 152 citations into it; #30,501 of 35,345 by trust here, #7,471 by citations
• Natural Language Processing — 52 citations into it; #4,963 of 5,688 by trust here, #3,107 by citations
• Safety, Robustness & Interpretability — 26 citations into it; #1,985 of 2,251 by trust here, #1,290 by citations
• Graph Learning — 21 citations into it
• Molecular Biology — 9 citations into it

Whom Wenqi Fan trusts overall

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

1. Geoffrey E. Hinton
2. Ruslan Salakhutdinov
3. Diederik P. Kingma
4. Ilya Sutskever
5. Yoshua Bengio
6. Alex Krizhevsky
7. Yann LeCun
8. Kaiming He
9. Alex Graves
10. Jimmy Ba

Who trusts Wenqi Fan overall

1. Le Wu
2. Chen Gao
3. Shiwen Wu
4. Xiangnan He
5. B. Khemani
6. Chuhan Wu
7. Bowen Jin
8. Pengyu Zhao
9. Jianxin Chang
10. Jianan Zhao

Papers from Wenqi Fan's perspective

Ordered by how strongly the researchers Wenqi Fan 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. Attention is All you Need (Neural Information Processing Systems)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Does Gender Matter? Towards Fairness in Dialogue Systems (2019) — 156 citations
• Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs (2023) — 443 citations
• Recommender Systems in the Era of Large Language Models (LLMs) (2023) — 541 citations
• Graph Neural Networks for Social Recommendation (2019) — 2,365 citations