Tzu-Yun Shann

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 20 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it
• Reinforcement Learning — 50 citations into it
• Management Science and Operations Research — 16 citations into it
• Optimization & Training — 9 citations into it

Whom Tzu-Yun Shann trusts overall

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

1. Richard S. Sutton
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Ronald J. Williams
5. Yoshua Bengio
6. Jimmy Ba
7. Alex Krizhevsky
8. J. Urgen Schmidhuber
9. Andrew Barto
10. Sepp Hochreiter

Papers from Tzu-Yun Shann's perspective

Ordered by how strongly the researchers Tzu-Yun Shann trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Deep Policy Inference Q-Network for Multi-Agent Systems (2017) — 93 citations
• Virtual-to-Real: Learning to Control in Visual Semantic Segmentation (2018) — 85 citations
• Diversity-Driven Exploration Strategy for Deep Reinforcement Learning (2018) — 147 citations