Hangyeol Yu

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 26 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 21 citations into it
• Probabilistic & Bayesian Methods — 18 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Generative Models — 6 citations into it
• Optimization & Training — 5 citations into it

Whom Hangyeol Yu trusts overall

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

1. Diederik P. Kingma
2. Danilo Jimenez Rezende
3. Chris J. Maddison
4. Andriy Mnih
5. Geoffrey E. Hinton
6. Dougal Maclaurin
7. Vikash K. Mansinghka
8. Shakir Mohamed
9. Max Welling
10. Ronald J. Williams

Papers from Hangyeol Yu's perspective

Ordered by how strongly the researchers Hangyeol Yu 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Reparameterization Gradient for Non-differentiable Models (2018) — 38 citations