Yi Luan

Artificial Intelligence · with Chang · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 110 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 137 citations into it; #10,246 of 35,345 by trust here, #5,834 by citations
• Information Retrieval & Search — 91 citations into it; #360 of 1,022 by trust here, #212 by citations
• Natural Language Processing — 66 citations into it
• Computer Vision and Pattern Recognition — 56 citations into it; #7,957 of 21,575 by trust here, #6,146 by citations
• Large Language Models — 25 citations into it
• Information Systems — 20 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Prompting & Reasoning — 9 citations into it

Whom Yi Luan trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Zhuyun Dai
4. Diederik P. Kingma
5. Jimmy Ba
6. Kyunghyun Cho
7. Kenton C. T. Lee
8. Ilya Sutskever
9. Rodrigo Nogueira
10. Alex Krizhevsky

Who trusts Yi Luan overall

1. Sheng-Chieh Lin
2. Luyu Gao
3. Sebastian Hofstätter
4. Ruiyang Ren
5. Gautier Izacard
6. Wayne Xin Zhao
7. Jing Liu
8. Yingqi Qu
9. Fabio Petroni
10. Jheng-Hong Yang

Papers from Yi Luan's perspective

Ordered by how strongly the researchers Yi Luan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Gradient-based learning applied to document recognition
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Sparse, Dense, and Attentional Representations for Text Retrieval (2020) — 509 citations
• Large Dual Encoders Are Generalizable Retrievers (2021) — 644 citations
• CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning (2021) — 113 citations
• Promptagator: Few-shot Dense Retrieval From 8 Examples (2022) — 350 citations