Hyun-woo Koh

Artificial Intelligence · 2021–2024

A researcher in the Science corpus: 3 papers; trusts 195 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 71 citations into it; #28,005 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 48 citations into it; #17,229 of 21,575 by trust here, #17,757 by citations
• Meta & Continual Learning — 29 citations into it; #870 of 1,041 by trust here, #897 by citations
• Optimization & Training — 20 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Radiology, Nuclear Medicine and Imaging — 6 citations into it
• Reinforcement Learning — 6 citations into it

Whom Hyun-woo Koh trusts overall

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

1. Rahaf Aljundi
2. Alex Krizhevsky
3. Yann LeCun
4. Geoffrey E. Hinton
5. Arslan Chaudhry
6. Yoshua Bengio
7. Kaiming He
8. Marc'Aurelio Ranzato
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Hyun-woo Koh overall

1. Byeonghwi Kim
2. Junsu Kim
3. Minhyuk Seo
4. Hoseong Cho
5. Liyuan Wang

Papers from Hyun-woo Koh's perspective

Ordered by how strongly the researchers Hyun-woo Koh 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Learning Multiple Layers of Features from Tiny Images
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (2021) — 89 citations
• Learning Equi-Angular Representations for Online Continual Learning (2024) — 25 citations
• Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries (2022) — 61 citations