Seungyong Moon

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 19 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it
• Reinforcement Learning — 17 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Seungyong Moon trusts overall

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

1. Aviral Kumar
2. Scott Fujimoto
3. Ian Osband
4. Richard S. Sutton
5. Sergey Levine
6. Justin Fu
7. Geoffrey E. Hinton
8. George Tucker
9. Marc G. Bellemare
10. Hado van Hasselt

Papers from Seungyong Moon's perspective

Ordered by how strongly the researchers Seungyong Moon 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. Human-level control through deep reinforcement learning
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Reinforcement Learning: An Introduction
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble (2021) — 412 citations