Junho Yim

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 4 papers; trusts 312 researchers and is trusted by 58 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 73 citations into it; #3,930 of 21,575 by trust here, #2,605 by citations
• Artificial Intelligence — 53 citations into it; #5,351 of 35,345 by trust here, #4,254 by citations
• Optimization & Training — 25 citations into it
• Model Architectures — 17 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Civil and Structural Engineering — 6 citations into it; #5,138 of 6,160 by trust here, #5,195 by citations
• Safety, Robustness & Interpretability — 5 citations into it

Whom Junho Yim trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Christian Szegedy
7. Karen Simonyan
8. Ilya Sutskever
9. Diederik P. Kingma
10. X. Zhang

Who trusts Junho Yim overall

1. Nikolaos Passalis
2. Zhiqiang Shen
3. Byeongho Heo
4. Seunghyun Lee
5. Xi Yin
6. Linfeng Zhang
7. Chenglin Yang
8. Mingi Ji
9. Seyed Iman Mirzadeh
10. Xiang Wu

Papers from Junho Yim's perspective

Ordered by how strongly the researchers Junho Yim 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning (2017) — 1,725 citations
• Rotating your face using multi-task deep neural network (2015) — 297 citations
• Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition (2015) — 676 citations
• NLNL: Negative Learning for Noisy Labels (2019) — 348 citations