Kang Min Yoo

Artificial Intelligence · 2017–2022

A researcher in the Science corpus: 6 papers; trusts 300 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 205 citations into it; #9,614 of 35,345 by trust here, #8,439 by citations
• Natural Language Processing — 132 citations into it; #1,939 of 5,688 by trust here, #2,203 by citations
• Computer Vision and Pattern Recognition — 72 citations into it
• Large Language Models — 47 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Prompting & Reasoning — 24 citations into it
• Model Architectures — 19 citations into it; #977 of 2,278 by trust here, #1,643 by citations

Whom Kang Min Yoo trusts overall

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

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

Who trusts Kang Min Yoo overall

1. Yutai Hou
2. Bohan Li
3. Zijian Zhao
4. Hwa-Yeon Kim
5. Biswesh Mohapatra
6. Yu Meng
7. Yichun Yin
8. Connor Shorten
9. Hao Zhou
10. Wanxiang Che

Papers from Kang Min Yoo's perspective

Ordered by how strongly the researchers Kang Min Yoo trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Learning to Compose Task-Specific Tree Structures (2017) — 196 citations
• What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers (2021) — 134 citations
• Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations (2022) — 153 citations
• Reward Optimization for Neural Machine Translation with Learned Metrics (2021) — 15 citations
• GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021) — 290 citations
• Data Augmentation for Spoken Language Understanding via Joint Variational Generation (2018) — 94 citations