Mina Lee

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 5 papers; trusts 65 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #15,050 of 35,345 by trust here, #2,350 by citations
• Natural Language Processing — 103 citations into it; #2,477 of 5,688 by trust here, #2,695 by citations
• Computer Vision and Pattern Recognition — 35 citations into it; #9,858 of 21,575 by trust here, #11,950 by citations
• Evaluation & Benchmarks — 19 citations into it
• Large Language Models — 13 citations into it
• Reinforcement Learning — 7 citations into it

Whom Mina Lee trusts overall

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

1. Ronald J. Williams
2. N. Storch
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Ilya Sutskever
7. Alex Krizhevsky
8. Elizabeth Clark
9. Yann LeCun
10. Oriol Vinyals

Who trusts Mina Lee overall

1. Michihiro Yasunaga
2. Terry Yue Zhuo
3. Percy Liang
4. Xingyao Wang
5. Wanyu Du
6. Timo Schick
7. Samuel Louvan
8. Marzena Karpinska
9. Lei Sha
10. Peter West

Papers from Mina Lee's perspective

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

Their papers

• On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
• Enabling Language Models to Fill in the Blanks (2020) — 212 citations
• CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities (2022) — 552 citations
• SPoC: Search-based Pseudocode to Code (2019) — 329 citations
• Learning Autocomplete Systems as a Communication Game (2019) — 14 citations