Jing Liu

Artificial Intelligence · with Wang · 2017–2022

A researcher in the Science corpus: 9 papers; trusts 465 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 362 citations into it; #19,942 of 35,345 by trust here, #4,627 by citations
• Natural Language Processing — 238 citations into it
• Information Retrieval & Search — 181 citations into it; #771 of 1,022 by trust here, #279 by citations
• Computer Vision and Pattern Recognition — 160 citations into it; #16,267 of 21,575 by trust here, #3,747 by citations
• Information Systems — 74 citations into it
• Evaluation & Benchmarks — 65 citations into it
• Large Language Models — 38 citations into it
• Representation & Self-Supervised Learning — 36 citations into it

Whom Jing Liu trusts overall

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

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

Who trusts Jing Liu overall

1. Wayne Xin Zhao
2. Luyu Gao
3. Ruiyang Ren
4. Jimmy J. Lin
5. Jinghui Qin
6. Zhanming Jie
7. Thibault Formal
8. Yining Hong
9. Bugeun Kim
10. Keshav Santhanam

Papers from Jing Liu's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
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

• RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering (2020) — 749 citations
• DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications (2017) — 309 citations
• Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (2019) — 152 citations
• Answer-focused and Position-aware Neural Question Generation (2018) — 188 citations
• Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification (2018) — 125 citations
• PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval (2021) — 103 citations
• RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking (2021) — 307 citations
• Dense Text Retrieval Based on Pretrained Language Models: A Survey (2022) — 325 citations
• Neural Math Word Problem Solver with Reinforcement Learning (2018) — 91 citations