Wei-Di Chang

Reinforcement Learning · 2017

A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 33 citations into it
• Artificial Intelligence — 25 citations into it
• Control and Systems Engineering — 9 citations into it

Whom Wei-Di Chang trusts overall

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

1. John Schulman
2. Richard S. Sutton
3. Sergey Levine
4. Geoffrey E. Hinton
5. Pieter Abbeel
6. Andrew Y. Ng
7. Diederik P. Kingma
8. Yoshua Bengio
9. Alex Krizhevsky
10. E. Todorov

Who trusts Wei-Di Chang overall

1. Sung Woo Park
2. Faraz Torabi
3. Peter Henderson
4. Christos Kaplanis

Papers from Wei-Di Chang's perspective

Ordered by how strongly the researchers Wei-Di Chang 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. GENERATIVE ADVERSARIAL NETS
8. ImageNet: A large-scale hierarchical image database
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

• Benchmark Environments for Multitask Learning in Continuous Domains (2017) — 42 citations
• OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning (2017) — 77 citations