Jang-Hyun Kim

Artificial Intelligence · with Song · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 422 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #34,996 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 46 citations into it; #21,361 of 21,575 by trust here, #15,529 by citations
• Reinforcement Learning — 18 citations into it
• Optimization & Training — 16 citations into it
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Jang-Hyun Kim trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Karen Simonyan
4. Radford M. Neal
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. X. Zhang
8. Ilya Sutskever
9. Ross B. Girshick
10. Andrew Zisserman

Who trusts Jang-Hyun Kim overall

1. Jihwan Bang
2. Linjun Zhang
3. Jie Qin
4. Xuhong Li
5. Sungmin Cha
6. Mingle Xu
7. Wonho Choo
8. Y. Yoo
9. Zhun Deng
10. Heesu Kim

Papers from Jang-Hyun Kim's perspective

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

Their papers

• Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity (2021) — 215 citations
• Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble (2021) — 412 citations
• Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup (2020) — 471 citations