Youngdong Kim

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 48 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it; #30,910 of 35,345 by trust here, #30,347 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #19,092 of 21,575 by trust here, #19,310 by citations
• Optimization & Training — 13 citations into it
• Model Architectures — 6 citations into it
• Civil and Structural Engineering — 5 citations into it; #5,136 of 6,160 by trust here, #5,195 by citations
• Safety, Robustness & Interpretability — 5 citations into it

Whom Youngdong Kim trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. X. Zhang
5. Dong-Hyun Lee
6. Arash Vahdat
7. Yoshua Bengio
8. J. Paul Brooks
9. Shaoqing Ren
10. Christian Szegedy

Who trusts Youngdong Kim overall

1. Görkem Algan
2. Mamshad Nayeem Rizve
3. Xingjun Ma
4. Ilkay Ulusoy
5. Kevin Duarte

Papers from Youngdong Kim's perspective

Ordered by how strongly the researchers Youngdong Kim 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• NLNL: Negative Learning for Noisy Labels (2019) — 348 citations