Yixing Fan

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 7 papers; trusts 142 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 155 citations into it; #3,702 of 35,345 by trust here, #5,028 by citations
• Natural Language Processing — 110 citations into it
• Information Retrieval & Search — 75 citations into it; #115 of 1,022 by trust here, #168 by citations
• Information Systems — 65 citations into it
• Computer Vision and Pattern Recognition — 44 citations into it
• Representation & Self-Supervised Learning — 21 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Model Architectures — 13 citations into it

Whom Yixing Fan trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Chengxiang Zhai
6. Tomas Mikolov
7. Jamie Callan
8. Yann LeCun
9. Robert Krovetz
10. W. Bruce Croft

Who trusts Yixing Fan overall

1. Zhuyun Dai
2. Chenyan Xiong
3. Andrew Yates
4. Sean MacAvaney
5. Jamie Callan
6. Sebastian Hofstätter
7. Jiafeng Guo
8. Rodrigo Nogueira
9. Bhaskar Mitra
10. Kai Hui

Papers from Yixing Fan's perspective

Ordered by how strongly the researchers Yixing Fan 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
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

• Learning to Control the Specificity in Neural Response Generation (2018) — 86 citations
• A Deep Relevance Matching Model for Ad-hoc Retrieval (2016) — 944 citations
• Pre-training Methods in Information Retrieval (2022) — 100 citations
• A Deep Look into Neural Ranking Models for Information Retrieval (2019) — 386 citations
• PROP: Pre-training with Representative Words Prediction for Ad-hoc Retrieval (2020) — 108 citations
• Modeling Diverse Relevance Patterns in Ad-hoc Retrieval (2018) — 94 citations
• Semantic Matching by Non-Linear Word Transportation for Information Retrieval (2016) — 77 citations