Daewoo Kim

Reinforcement Learning · 2019

A researcher in the Science corpus: 2 papers; trusts 20 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 61 citations into it; #893 of 2,242 by trust here, #1,139 by citations
• Artificial Intelligence — 45 citations into it; #10,738 of 35,345 by trust here, #12,387 by citations
• Game & Decision Theory — 14 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Computer Networks and Communications — 8 citations into it
• LLM Agents & Tool Use — 7 citations into it

Whom Daewoo Kim trusts overall

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

1. Jakob N. Foerster
2. Geoffrey E. Hinton
3. Richard S. Sutton
4. G. Tesauro
5. Peter Stone
6. David Silver
7. Carlos Guestrin
8. Jayesh K. Gupta
9. Volodymyr Mnih
10. Yoshua Bengio

Who trusts Daewoo Kim overall

1. Sai Qian Zhang
2. Tonghan Wang
3. Kyunghwan Son
4. Jianhao Wang
5. Jieyu Lin
6. Christian Schröder de Witt
7. Qi Zhang
8. Tabish Rashid
9. Bei Peng
10. Chao Yu

Papers from Daewoo Kim's perspective

Ordered by how strongly the researchers Daewoo 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. Human-level control through deep reinforcement learning
10. Asynchronous Methods for Deep Reinforcement Learning

Their papers

• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning (2019) — 1,068 citations
• Learning to Schedule Communication in Multi-agent Reinforcement Learning (2019) — 257 citations