Do-Hyung Kim

Computer Vision and Pattern Recognition · with Choi · 2018

A researcher in the Science corpus: 2 papers; trusts 11 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 14 citations into it
• Artificial Intelligence — 13 citations into it
• Periodontics — 6 citations into it

Whom Do-Hyung Kim trusts overall

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

1. Karen Simonyan
2. Geoffrey E. Hinton
3. Vinod Nair
4. Wanli Ouyang
5. Andrew Zisserman
6. Christian Szegedy
7. Alex Krizhevsky
8. Xingchao Peng
9. Kaiming He
10. Xiaogang Wang

Papers from Do-Hyung Kim's perspective

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

Their papers

• Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. (2018) — 832 citations
• Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm (2018) — 406 citations