Jaewoo Kang

Artificial Intelligence · 2003–2022

A researcher in the Science corpus: 15 papers; trusts 620 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 292 citations into it; #6,806 of 35,345 by trust here, #2,793 by citations
• Natural Language Processing — 183 citations into it; #1,666 of 5,688 by trust here, #811 by citations
• Computer Vision and Pattern Recognition — 120 citations into it; #12,459 of 21,575 by trust here, #7,092 by citations
• Molecular Biology — 69 citations into it; #4,782 of 17,773 by trust here, #2,783 by citations
• Graph Learning — 63 citations into it
• Information Retrieval & Search — 51 citations into it; #696 of 1,022 by trust here, #448 by citations
• Representation & Self-Supervised Learning — 44 citations into it; #3,033 of 3,145 by trust here, #2,454 by citations
• Evaluation & Benchmarks — 39 citations into it

Whom Jaewoo Kang trusts overall

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

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

Who trusts Jaewoo Kang overall

1. Jinhyuk Lee
2. Brian Babcock
3. Lukasz Golab
4. M. V. Koroteev
5. Robin Jia
6. Charu C. Aggarwal
7. Mayur Datar
8. Iz Beltagy
9. Jianan Zhao
10. M. Tamer Özsu

Papers from Jaewoo Kang's perspective

Ordered by how strongly the researchers Jaewoo Kang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• BioBERT: a pre-trained biomedical language representation model for biomedical text mining (2019) — 7,641 citations
• Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering (2018) — 90 citations
• Self-Attention Graph Pooling (2019) — 1,340 citations
• Learning Dense Representations of Phrases at Scale (2020) — 130 citations
• Graph Transformer Networks (2019) — 1,382 citations
• Look at the First Sentence: Position Bias in Question Answering (2020) — 123 citations
• Mut2Vec: distributed representation of cancerous mutations (2018) — 48 citations
• Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen (2019) — 299 citations
• CollaboNet: collaboration of deep neural networks for biomedical named entity recognition (2018) — 147 citations
• DyGRAIN: An Incremental Learning Framework for Dynamic Graphs (2022) — 39 citations
• Learning by Semantic Similarity Makes Abstractive Summarization Better (2020) — 18 citations
• Contextualized Sparse Representation with Rectified N-Gram Attention for Open-Domain Question Answering (2019) — 53 citations
• Contextualized Sparse Representations for Real-Time Open-Domain Question Answering (2019) — 31 citations
• DSigDB: drug signatures database for gene set analysis (2015) — 663 citations
• Evaluating window joins over unbounded streams (2003) — 418 citations