Yusu Qian

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it; #14,921 of 35,345 by trust here, #18,586 by citations
• Natural Language Processing — 12 citations into it; #2,735 of 5,688 by trust here, #3,687 by citations
• Safety, Robustness & Interpretability — 12 citations into it; #999 of 2,251 by trust here, #1,589 by citations
• Representation & Self-Supervised Learning — 5 citations into it

Whom Yusu Qian trusts overall

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

1. Shibamouli Lahiri
2. Jieyu Zhao
3. Moshe Koppel
4. Jonathan Schler
5. Ilya Sutskever
6. Shlomo Engelson Argamon
7. Kai-Wei Chang
8. Tolga Bolukbasi
9. Kaiji Lu
10. Jeffrey Pennington

Who trusts Yusu Qian overall

1. Karolina Stańczak
2. Angelina Wang
3. Emily Dinan
4. Soumya Barikeri
5. Isabelle Augenstein
6. Jiaxu Zhao
7. Olga Russakovsky
8. Angela Fan
9. Anne Lauscher
10. Jesse Vig

Papers from Yusu Qian's perspective

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

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

Their papers

• Reducing Gender Bias in Word-Level Language Models with a Gender-Equalizing Loss Function (2019) — 105 citations
• Gender Stereotypes Differ between Male and Female Writings (2019) — 6 citations