Dong-Ki Kim

Artificial Intelligence · with How · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 40 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 112 citations into it
• Reinforcement Learning — 96 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it

Whom Dong-Ki Kim trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Pieter Abbeel
5. J. Urgen Schmidhuber
6. Diederik P. Kingma
7. Alex Graves
8. Ronald J. Williams
9. John Schulman
10. Alex Krizhevsky

Who trusts Dong-Ki Kim overall

1. Khimya Khetarpal
2. Matthew Riemer
3. Xueguang Lyu
4. Yongyuan Liang

Papers from Dong-Ki Kim's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Learning Hierarchical Teaching Policies for Cooperative Agents (2019) — 33 citations
• A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning (2020) — 69 citations
• Influencing Long-Term Behavior in Multiagent Reinforcement Learning (2022) — 28 citations
• Context-Specific Representation Abstraction for Deep Option Learning (2021) — 16 citations
• Learning to Teach in Cooperative Multiagent Reinforcement Learning (2018) — 145 citations