Kyong Hwan Jin

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 262 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 80 citations into it; #6,144 of 21,575 by trust here, #4,865 by citations
• Computational Mechanics — 32 citations into it
• Radiology, Nuclear Medicine and Imaging — 24 citations into it; #3,139 of 3,265 by trust here, #2,536 by citations
• Media Technology — 23 citations into it; #1,029 of 3,621 by trust here, #505 by citations
• Artificial Intelligence — 22 citations into it
• Biomedical Engineering — 21 citations into it; #10,320 of 10,734 by trust here, #8,278 by citations
• Model Architectures — 10 citations into it
• Mathematical Physics — 8 citations into it

Whom Kyong Hwan Jin trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Alex Krizhevsky
4. Kaiming He
5. Yoshua Bengio
6. Karol Gregor
7. Ilya Sutskever
8. Diederik P. Kingma
9. Ross B. Girshick
10. Andrea Vedaldi

Who trusts Kyong Hwan Jin overall

1. Jonas Adler
2. Yoseob Han
3. Jong Chul Ye
4. Ozan Öktem
5. Yan Yang
6. Andreas Hauptmann
7. Vishal Monga
8. Michael T. McCann
9. Sarah Jane Hamilton
10. Zhun Wei

Papers from Kyong Hwan Jin's perspective

Ordered by how strongly the researchers Kyong Hwan Jin 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Deep Convolutional Neural Network for Inverse Problems in Imaging (2016) — 2,439 citations
• CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction (2017) — 397 citations
• Convolutional Neural Networks for Inverse Problems in Imaging: A Review (2017) — 665 citations
• Fast and robust segmentation of the striatum using deep convolutional neural networks. (2016) — 68 citations