Kai Hui

Artificial Intelligence · 2017–2023

A researcher in the Science corpus: 12 papers; trusts 104 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 254 citations into it; #9,261 of 35,345 by trust here, #6,378 by citations
• Natural Language Processing — 132 citations into it
• Information Retrieval & Search — 86 citations into it; #297 of 1,022 by trust here, #358 by citations
• Computer Vision and Pattern Recognition — 78 citations into it; #9,510 of 21,575 by trust here, #7,938 by citations
• Evaluation & Benchmarks — 64 citations into it; #3,065 of 3,147 by trust here, #3,037 by citations
• Information Systems — 64 citations into it; #764 of 8,771 by trust here, #1,189 by citations
• Large Language Models — 54 citations into it
• Representation & Self-Supervised Learning — 26 citations into it

Whom Kai Hui trusts overall

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

1. Jiafeng Guo
2. W. Bruce Croft
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Liang Pang
6. Bhaskar Mitra
7. Ilya Sutskever
8. Yoshua Bengio
9. Chenyan Xiong
10. Tomas Mikolov

Who trusts Kai Hui overall

1. Sebastian Hofstätter
2. Sean MacAvaney
3. Rodrigo Nogueira
4. Ryan T. McDonald
5. Andrew Yates
6. Jyun-Yu Jiang
7. Jimmy J. Lin
8. Chenyan Xiong
9. Donald Metzler
10. Allan Hanbury

Papers from Kai Hui's perspective

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

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

Their papers

• ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning (2021) — 240 citations
• Transformer Memory as a Differentiable Search Index (2022) — 444 citations
• NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval (2018) — 74 citations
• Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting (2023) — 461 citations
• Learning List-Level Domain-Invariant Representations for Ranking (2022)
• RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses (2022)
• BERT-QE: Contextualized Query Expansion for Document Re-ranking (2020) — 121 citations
• Contextualized PACRR for Complex Answer Retrieval (2017) — 9 citations
• Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval (2017) — 125 citations
• PACRR: A Position-Aware Neural IR Model for Relevance Matching (2017) — 165 citations
• Low-Cost Preference Judgment via Ties (2017) — 9 citations
• Content-Based Weak Supervision for Ad-Hoc Re-Ranking (2017) — 46 citations