Seungchan Kim

Reinforcement Learning · 2019

A researcher in the Science corpus: 1 paper; trusts 31 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 40 citations into it
• Artificial Intelligence — 34 citations into it
• Statistical Learning Theory — 7 citations into it
• Management Science and Operations Research — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it

Whom Seungchan Kim trusts overall

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

1. Richard S. Sutton
2. Satinder Singh
3. Erik Talvitie
4. Andrew Barto
5. Martin L. Puterman
6. Doina Precup
7. Geoffrey E. Hinton
8. David Silver
9. Volodymyr Mnih
10. Peter L. Bartlett

Papers from Seungchan Kim's perspective

Ordered by how strongly the researchers Seungchan Kim 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Reinforcement Learning: An Introduction
6. Long Short-Term Memory
7. Asynchronous Methods for Deep Reinforcement Learning
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

• Combating the Compounding-Error Problem with a Multi-step Model (2019) — 71 citations