Beomsu Kim

Artificial Intelligence · with Jeon · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 37 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it
• Safety, Robustness & Interpretability — 23 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it

Whom Beomsu Kim trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Matthew D. Zeiler
5. Ian J. Goodfellow
6. Yoshua Bengio
7. Rob Fergus
8. Nicholas Carlini
9. Diederik P. Kingma
10. Christian Szegedy

Papers from Beomsu Kim's perspective

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

Their papers

• Noise-adding Methods of Saliency Map as Series of Higher Order Partial Derivative (2018) — 30 citations
• Bridging Adversarial Robustness and Gradient Interpretability (2019) — 45 citations