Chae-Bong Sohn

Reinforcement Learning · 2018

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

Subfields

• Reinforcement Learning — 12 citations into it

Whom Chae-Bong Sohn trusts overall

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

1. Ming Tan
2. Maxim Egorov Stanford
3. Richard S. Sutton

Papers from Chae-Bong Sohn's perspective

Ordered by how strongly the researchers Chae-Bong Sohn 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. Attention is All you Need (Neural Information Processing Systems)
3. Continuous control with deep reinforcement learning (International Conference on Learning Representations)
4. Playing Atari with Deep Reinforcement Learning (arXiv.org)
5. Policy Gradient Methods for Reinforcement Learning with Function Approximation
6. ImageNet classification with deep convolutional neural networks
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Markov Decision Processes: Discrete Stochastic Dynamic Programming

Their papers

• Deep Multi-Agent Reinforcement Learning with Relevance Graphs (2018) — 50 citations