Dong-Ho Lee

Artificial Intelligence · with Ren · 2019–2022

A researcher in the Science corpus: 5 papers; trusts 109 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 99 citations into it
• Natural Language Processing — 82 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it
• Evaluation & Benchmarks — 29 citations into it
• Large Language Models — 24 citations into it
• Information Retrieval & Search — 8 citations into it

Whom Dong-Ho Lee trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Kaiming He
7. Yann LeCun
8. Dzmitry Bahdanau
9. Jacob Devlin
10. Alex Graves

Who trusts Dong-Ho Lee overall

1. Stephen H. Bach
2. Azal Ahmad Khan

Papers from Dong-Ho Lee's perspective

Ordered by how strongly the researchers Dong-Ho 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. 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. 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

• ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data (2020) — 60 citations
• Pre-training Text-to-Text Transformers for Concept-centric Common Sense (2020) — 73 citations
• AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging (2019) — 38 citations
• LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation (2020) — 22 citations
• Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer (2022) — 13 citations