Tegg Taekyong Sung

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 Tegg Taekyong Sung trusts overall

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

1. Ming Tan
2. Maxim Egorov Stanford
3. Richard S. Sutton
4. Volodymyr Mnih
5. Tingwu Wang

Papers from Tegg Taekyong Sung's perspective

Ordered by how strongly the researchers Tegg Taekyong Sung 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. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Continuous control with deep reinforcement learning (International Conference on Learning Representations)
8. Playing Atari with Deep Reinforcement Learning (arXiv.org)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

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