Yeongil Ko

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it
• Privacy & Security — 20 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Computational Theory and Mathematics — 12 citations into it

Whom Yeongil Ko trusts overall

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

1. Craig Gentry
2. Alex Krizhevsky
3. Zvika Brakerski
4. Karen Simonyan
5. Kaiming He
6. Yann LeCun
7. Geoffrey E. Hinton
8. Payman Mohassel
9. Frederik Vercauteren
10. Shai Halevi

Papers from Yeongil Ko's perspective

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

Their papers

• Cheetah: Optimizing and Accelerating Homomorphic Encryption for Private Inference (2020) — 187 citations