Rui Wang

Artificial Intelligence · with Clune · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 101 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 163 citations into it; #11,564 of 35,345 by trust here, #11,750 by citations
• Reinforcement Learning — 95 citations into it; #1,509 of 2,242 by trust here, #1,545 by citations
• Computer Vision and Pattern Recognition — 52 citations into it
• Optimization & Training — 23 citations into it
• Sociology and Political Science — 20 citations into it
• Control and Systems Engineering — 17 citations into it
• Computational Theory and Mathematics — 15 citations into it
• Management Science and Operations Research — 12 citations into it

Whom Rui Wang trusts overall

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

1. Kenneth O. Stanley
2. David R Ha
3. J. Urgen Schmidhuber
4. Joel Lehman
5. Ronald J. Williams
6. Diederik P. Kingma
7. Geoffrey E. Hinton
8. Jeff Clune
9. Richard S. Sutton
10. John Schulman

Who trusts Rui Wang overall

1. Jeff Clune
2. Minqi Jiang
3. Michael Dennis
4. Jiale Zhi
5. Sebastian Risi
6. Linxi (Jim) Fan
7. Connor Shorten
8. Natasha Jaques
9. Sae-Young Chung
10. Guanzhi Wang

Papers from Rui Wang's perspective

Ordered by how strongly the researchers Rui Wang 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions (2019) — 302 citations
• Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions (2020) — 155 citations
• An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents (2018) — 55 citations
• POET: open-ended coevolution of environments and their optimized solutions (2019) — 103 citations
• Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods (2020) — 12 citations