Sunghwan Joo

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 164 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #23,452 of 35,345 by trust here, #22,463 by citations
• Safety, Robustness & Interpretability — 29 citations into it; #1,536 of 2,251 by trust here, #1,857 by citations
• Computer Vision and Pattern Recognition — 20 citations into it
• Biophysics — 6 citations into it

Whom Sunghwan Joo trusts overall

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

1. Karen Simonyan
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Andrew Zisserman
6. Matthew D. Zeiler
7. Yoshua Bengio
8. Diederik P. Kingma
9. Yann LeCun
10. Rob Fergus

Who trusts Sunghwan Joo overall

1. Sarah Wiegreffe
2. Botty Dimanov
3. Laura Rieger

Papers from Sunghwan Joo's perspective

Ordered by how strongly the researchers Sunghwan Joo 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 Large Scale Visual Recognition Challenge
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Long Short-Term Memory
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Fooling Neural Network Interpretations via Adversarial Model Manipulation (2019) — 241 citations