Sharon Qian

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 42 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 121 citations into it; #18,327 of 35,345 by trust here, #19,439 by citations
• Natural Language Processing — 68 citations into it; #3,663 of 5,688 by trust here, #2,220 by citations
• Safety, Robustness & Interpretability — 52 citations into it; #1,896 of 2,251 by trust here, #1,119 by citations
• Causal Inference & Discovery — 22 citations into it
• Computational Theory and Mathematics — 18 citations into it
• Algorithms & Complexity — 16 citations into it; #322 of 516 by trust here, #484 by citations
• Computer Vision and Pattern Recognition — 15 citations into it
• Statistics and Probability — 14 citations into it

Whom Sharon Qian trusts overall

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

1. Eric Balkanski
2. Yaron Singer
3. Ethan R. Elenberg
4. Andreas Krause
5. Matthew Fahrbach
6. Abhimanyu Das
7. Yatao Bian
8. Vahab S. Mirrokni
9. Judea Pearl
10. A. Dimakis

Who trusts Sharon Qian overall

1. Jesse Vig
2. Yonatan Belinkov
3. Sebastian Gehrmann
4. Kevin Meng
5. Amir Feder
6. Matthew Fahrbach

Papers from Sharon Qian's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
5. ImageNet classification with deep convolutional neural networks
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Gradient-based learning applied to document recognition
9. ImageNet: A large-scale hierarchical image database
10. Language Models are Unsupervised Multitask Learners

Their papers

• Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias (2020) — 211 citations
• Fast Parallel Algorithms for Feature Selection (2019) — 8 citations
• Investigating Gender Bias in Language Models Using Causal Mediation Analysis (2020) — 725 citations