Eunhee Kang

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 22 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 38 citations into it
• Biomedical Engineering — 19 citations into it; #2,296 of 10,734 by trust here, #4,948 by citations
• Radiology, Nuclear Medicine and Imaging — 18 citations into it; #741 of 3,265 by trust here, #1,399 by citations
• Artificial Intelligence — 16 citations into it
• Optimization & Training — 8 citations into it
• Model Architectures — 6 citations into it

Whom Eunhee Kang trusts overall

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

1. Antonin Chambolle
2. Marcel Beister
3. Kaiming He
4. Andrea Vedaldi
5. Geoffrey E. Hinton
6. Jong Chul Ye
7. Alex Krizhevsky
8. Heinz H. Bauschke
9. Kostadin Dabov
10. Jian-Feng Cai

Who trusts Eunhee Kang overall

1. Jong Chul Ye
2. Jonas Adler
3. Yoseob Han
4. Xin Yi
5. Hyungjin Chung
6. Maciej A. Mazurowski
7. Jelmer M. Wolterink
8. Jaejun Yoo
9. Ozan Öktem
10. Berkman Sahiner

Papers from Eunhee Kang's perspective

Ordered by how strongly the researchers Eunhee Kang 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction (2016) — 820 citations
• Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network (2017) — 306 citations