Haejun Lee

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 283 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 173 citations into it
• Natural Language Processing — 141 citations into it
• Computer Vision and Pattern Recognition — 93 citations into it
• Information Retrieval & Search — 50 citations into it
• Large Language Models — 30 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Model Architectures — 13 citations into it
• Representation & Self-Supervised Learning — 12 citations into it

Whom Haejun Lee trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Alex Graves
7. Yann LeCun
8. Kaiming He
9. Jimmy Ba
10. Ruslan Salakhutdinov

Who trusts Haejun Lee overall

1. Yuanmeng Yan
2. Zhengbao Jiang
3. Omar Khattab
4. Timo Schick
5. Mark Anderson
6. Yunchang Zhu

Papers from Haejun Lee's perspective

Ordered by how strongly the researchers Haejun 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. Attention is All you Need (Neural Information Processing Systems)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• Learning to Generate Questions by Learning to Recover Answer-containing Sentences (2021) — 19 citations
• SLM: Learning a Discourse Language Representation with Sentence Unshuffling (2020) — 53 citations
• You Only Need One Model for Open-domain Question Answering (2021) — 31 citations
• Retrieve, Rerank, Read, then Iterate: Answering Open-Domain Questions of Arbitrary Complexity from Text (2020) — 17 citations
• A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension (2018) — 23 citations
• On-Device Neural Language Model Based Word Prediction (2018) — 32 citations
• Quote Recommendation in Dialogue using Deep Neural Network (2016) — 51 citations