Jongmin Lee
Reinforcement Learning · 2021–2022
A researcher in the Science corpus: 2 papers; trusts 72 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Reinforcement Learning — 32 citations into it
- Artificial Intelligence — 30 citations into it
- Management Science and Operations Research — 13 citations into it
- Computational Theory and Mathematics — 8 citations into it
- Electrical and Electronic Engineering — 6 citations into it
- Control and Systems Engineering — 5 citations into it
- Statistical Learning Theory — 5 citations into it
Whom Jongmin Lee trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard S. Sutton
- Sergey Levine
- Aviral Kumar
- Ofir Nachum
- Geoffrey E. Hinton
- Diederik P. Kingma
- E. Todorov
- Yoshua Bengio
- Justin Fu
- Lihong Li
Who trusts Jongmin Lee overall
Papers from Jongmin Lee's perspective
Ordered by how strongly the researchers Jongmin Lee trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Human-level control through deep reinforcement learning
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- GPT-Critic: Offline Reinforcement Learning for End-to-End Task-Oriented Dialogue Systems (2022) — 46 citations
- Representation Balancing Offline Model-based Reinforcement Learning (2021) — 52 citations