Youngkyu Hong

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 46 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it
• Computer Vision and Pattern Recognition — 29 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Youngkyu Hong trusts overall

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

1. Alex Krizhevsky
2. Ross B. Girshick
3. Kaiming He
4. Radford M. Neal
5. Karen Simonyan
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Andrew Zisserman
9. Diederik P. Kingma
10. Yann LeCun

Papers from Youngkyu Hong's perspective

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

Their papers

• Disentangling Label Distribution for Long-tailed Visual Recognition (2020) — 317 citations