Jiaxin Mao

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 7 papers; trusts 86 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #21,345 of 35,345 by trust here, #15,004 by citations
• Information Retrieval & Search — 79 citations into it
• Computer Vision and Pattern Recognition — 46 citations into it
• Natural Language Processing — 43 citations into it
• Information Systems — 37 citations into it
• Large Language Models — 21 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Jiaxin Mao trusts overall

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

1. Zhuyun Dai
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Rodrigo Nogueira
6. Kyunghyun Cho
7. Jamie Callan
8. Alex Krizhevsky
9. Ilya Sutskever
10. Kaiming He

Who trusts Jiaxin Mao overall

1. Wayne Xin Zhao
2. Jingtao Zhan
3. Keshav Santhanam
4. Jing Liu
5. Omar Khattab
6. Jimmy J. Lin
7. C. Clarke
8. Ruiyang Ren
9. Sebastian Hofstätter
10. Zhuosheng Zhang

Papers from Jiaxin Mao's perspective

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

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

Their papers

• Optimizing Dense Retrieval Model Training with Hard Negatives (2021) — 352 citations
• Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance (2021) — 97 citations
• An Analysis of BERT in Document Ranking (2020) — 55 citations
• Leveraging Passage-level Cumulative Gain for Document Ranking (2020) — 40 citations
• RepBERT: Contextualized Text Embeddings for First-Stage Retrieval (2020) — 140 citations
• Preference-based Evaluation Metrics for Web Image Search (2020) — 14 citations
• Human Behavior Inspired Machine Reading Comprehension (2019) — 44 citations