Hongrae Lee

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 6 papers; trusts 185 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 261 citations into it; #18,978 of 35,345 by trust here, #3,229 by citations
• Natural Language Processing — 200 citations into it; #3,269 of 5,688 by trust here, #832 by citations
• Computer Vision and Pattern Recognition — 85 citations into it
• Evaluation & Benchmarks — 45 citations into it
• Information Systems — 30 citations into it
• Model Architectures — 19 citations into it
• Management Science and Operations Research — 14 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Hongrae Lee trusts overall

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

1. Alex Graves
2. Yoshua Bengio
3. Ronald J. Williams
4. Diederik P. Kingma
5. Sepp Hochreiter
6. Geoffrey E. Hinton
7. Ilya Sutskever
8. Jimmy Ba
9. Yann LeCun
10. J. Urgen Schmidhuber

Who trusts Hongrae Lee overall

1. Tsu-Jui Fu
2. Samuel Kiegeland
3. Andrea Banino
4. Jin Tao
5. Minjoon Seo
6. Keyi Yu
7. Zhe Gan
8. Yizeng Han
9. Adams Wei Yu

Papers from Hongrae Lee's perspective

Ordered by how strongly the researchers Hongrae Lee 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. 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. 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

• LaMDA: Language Models for Dialog Applications (2022) — 1,891 citations
• Learning to Skim Text (2017) — 128 citations
• Attributed Text Generation via Post-hoc Research and Revision (2022) — 27 citations
• RARR: Researching and Revising What Language Models Say, Using Language Models (2022) — 339 citations
• Natural language to SQL: Where are we today? (2020) — 156 citations
• Ten Years of WebTables (2018) — 66 citations