Sung-Ik Choi

Reinforcement Learning · 2018

A researcher in the Science corpus: 1 paper; trusts 18 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 18 citations into it
• Artificial Intelligence — 13 citations into it

Whom Sung-Ik Choi trusts overall

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

1. Marc G. Bellemare
2. Ben J. A. Kröse
3. Tom Schaul
4. Richard S. Sutton
5. Yann LeCun
6. Hado van Hasselt
7. R´emi Munos
8. Simon S. Haykin
9. David Silver
10. Máté Lengyel

Who trusts Sung-Ik Choi overall

1. José Antonio Arjona-Medina
2. Sanmit Narvekar

Papers from Sung-Ik Choi's perspective

Ordered by how strongly the researchers Sung-Ik Choi trusts endorse each paper. Same corpus, a different order for every researcher.

1. Gradient-based learning applied to document recognition
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Human-level control through deep reinforcement learning
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. The Arcade Learning Environment: An Evaluation Platform for General Agents
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

• Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update (2018) — 83 citations