Young-Ik Kim

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it
• Generative Models — 11 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it

Whom Young-Ik Kim trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Jimmy Ba
4. Aäron van den Oord
5. Yoshua Bengio
6. Alex Krizhevsky
7. Karen Simonyan
8. Kaiming He
9. Phillip Isola
10. Yann LeCun

Who trusts Young-Ik Kim overall

1. Songxiang Liu
2. Xu Tan
3. Takuhiro Kaneko
4. Takuma Okamoto
5. Cong Wang
6. Jaeseong You
7. Taejun Bak

Papers from Young-Ik Kim's perspective

Ordered by how strongly the researchers Young-Ik Kim trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network (2020) — 78 citations