Jaejun Lee
Artificial Intelligence · 2019–2020
A researcher in the Science corpus: 2 papers; trusts 30 researchers and is trusted by 3 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 32 citations into it; #25,637 of 35,345 by trust here, #15,542 by citations
- Natural Language Processing — 26 citations into it
- Computer Vision and Pattern Recognition — 13 citations into it; #15,888 of 21,575 by trust here, #11,498 by citations
- Large Language Models — 11 citations into it
- Evaluation & Benchmarks — 10 citations into it
Whom Jaejun Lee trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Jacob Devlin
- Jimmy Ba
- Yoshua Bengio
- William B. Dolan
- Alex Krizhevsky
- Ashish Vaswani
- Ilya Sutskever
- Tomas Mikolov
Who trusts Jaejun Lee overall
Papers from Jaejun Lee's perspective
Ordered by how strongly the researchers Jaejun Lee trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Long Short-Term Memory
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference (2020) — 534 citations
- What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning (2019) — 153 citations