Tao Li

Artificial Intelligence · with Srikumar · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 84 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #12,905 of 35,345 by trust here, #13,962 by citations
• Natural Language Processing — 81 citations into it; #4,259 of 5,688 by trust here, #3,574 by citations
• Safety, Robustness & Interpretability — 49 citations into it; #876 of 2,251 by trust here, #999 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Large Language Models — 7 citations into it
• Safety Research — 6 citations into it

Whom Tao Li trusts overall

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

1. Diederik P. Kingma
2. Sunipa Dev
3. Geoffrey E. Hinton
4. Jieyu Zhao
5. Yoshua Bengio
6. Ilya Sutskever
7. Samuel R. Bowman
8. Richard Socher
9. Christopher D. Manning
10. Jeffrey Pennington

Who trusts Tao Li overall

1. Sunipa Dev
2. Nora Kassner
3. Akari Asai
4. Jieyu Zhao
5. Daniel Khashabi
6. Oyvind Tafjord
7. Haoyu Wang
8. Hinrich Schütze
9. Tushar Khot
10. Hannaneh Hajishirzi

Papers from Tao Li's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
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. 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. 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

• On Measuring and Mitigating Biased Inferences of Word Embeddings (2019) — 198 citations
• A Logic-Driven Framework for Consistency of Neural Models (2019) — 120 citations
• OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings (2020) — 60 citations
• UNQOVERing Stereotypical Biases via Underspecified Questions (2020) — 167 citations