Juseung Yun

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; #30,911 of 35,345 by trust here, #30,347 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #19,093 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; #5,137 of 6,160 by trust here, #5,195 by citations
• Safety, Robustness & Interpretability — 5 citations into it

Whom Juseung Yun 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. Yoshua Bengio
6. Dong-Hyun Lee
7. Arash Vahdat
8. Shaoqing Ren
9. Diederik P. Kingma
10. Christian Szegedy

Who trusts Juseung Yun overall

1. Görkem Algan

Papers from Juseung Yun's perspective

Ordered by how strongly the researchers Juseung Yun 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. Learning Multiple Layers of Features from Tiny Images
4. Going deeper with convolutions
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
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