Seongok Ryu

Materials Chemistry · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Materials Chemistry — 20 citations into it
• Artificial Intelligence — 9 citations into it
• Generative Models — 9 citations into it

Whom Seongok Ryu trusts overall

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

1. Diederik P. Kingma
2. Sepp Hochreiter
3. Marcus Olivecrona
4. Thomas Blaschke
5. Max Welling
6. J. Urgen Schmidhuber
7. Geoffrey E. Hinton
8. Gabriel Lima Guimaraes
9. John J. Irwin
10. Artur Kadurin

Who trusts Seongok Ryu overall

1. Tomohide Masuda
2. Nathan Brown
3. Xiao Wang

Papers from Seongok Ryu's perspective

Ordered by how strongly the researchers Seongok Ryu trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation (2019) — 366 citations
• Molecular generative model based on conditional variational autoencoder for de novo molecular design (2018) — 408 citations