Zongqing Lu

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 132 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 89 citations into it; #8,654 of 35,345 by trust here, #11,327 by citations
• Reinforcement Learning — 74 citations into it; #809 of 2,242 by trust here, #1,241 by citations
• Computer Vision and Pattern Recognition — 25 citations into it
• Computational Theory and Mathematics — 18 citations into it
• LLM Agents & Tool Use — 15 citations into it; #22 of 124 by trust here, #50 by citations
• Computer Science Applications — 13 citations into it
• Electrical and Electronic Engineering — 10 citations into it
• Management Science and Operations Research — 10 citations into it

Whom Zongqing Lu trusts overall

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

1. Jakob N. Foerster
2. Tuomas Haarnoja
3. Pieter Abbeel
4. Geoffrey E. Hinton
5. Volodymyr Mnih
6. Richard S. Sutton
7. Diederik P. Kingma
8. Sergey Levine
9. Yoshua Bengio
10. Ryan J. Lowe

Who trusts Zongqing Lu overall

1. Afshin Oroojlooyjadid
2. Yuhang Zhao
3. Yongyuan Liang
4. Davood Hajinezhad
5. Heechang Ryu
6. Kyunghwan Son
7. Akshat Agarwal
8. Chenghao Li
9. Yen-Cheng Liu
10. Jiechuan Jiang

Papers from Zongqing Lu's perspective

Ordered by how strongly the researchers Zongqing Lu 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. Human-level control through deep reinforcement learning

Their papers

• Learning Attentional Communication for Multi-Agent Cooperation (2018) — 609 citations
• FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning (2021) — 93 citations
• The Emergence of Individuality in Multi-Agent Reinforcement Learning (2020) — 5 citations
• Graph Convolutional Reinforcement Learning (2018) — 435 citations
• Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation (2018) — 15 citations