Jun Ki Lee

Reinforcement Learning · 2019

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 26 citations into it
• Artificial Intelligence — 18 citations into it
• Control and Systems Engineering — 6 citations into it

Whom Jun Ki Lee trusts overall

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

1. Geoffrey E. Hinton
2. Pieter Abbeel
3. David E. Rumelhart
4. Richard S. Sutton
5. Chelsea Finn
6. Andrew Y. Ng
7. Ronald J. Williams
8. Diederik P. Kingma
9. Sergey Levine
10. Stéphane Ross

Who trusts Jun Ki Lee overall

1. Lin Guan

Papers from Jun Ki Lee's perspective

Ordered by how strongly the researchers Jun Ki Lee 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Reinforcement Learning from Policy-Dependent Human Feedback (2019) — 111 citations