Wulong Liu

Artificial Intelligence · with Hao · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 18 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it
• Reinforcement Learning — 47 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• Game & Decision Theory — 7 citations into it

Whom Wulong Liu trusts overall

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

1. Ming Tan
2. Richard S. Sutton
3. Jakob N. Foerster
4. Geoffrey E. Hinton
5. Tabish Rashid
6. J. Urgen Schmidhuber
7. Andrew Barto
8. Alex Graves
9. Yoshua Bengio
10. Diederik P. Kingma

Papers from Wulong Liu's perspective

Ordered by how strongly the researchers Wulong Liu 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Human-level control through deep reinforcement learning
8. ImageNet: A large-scale hierarchical image database
9. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
10. Continuous control with deep reinforcement learning (International Conference on Learning Representations)

Their papers

• Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning (2020) — 61 citations
• Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning (2020) — 236 citations
• Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning (2019) — 85 citations