Guannan Qu

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 4 papers; trusts 53 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 68 citations into it; #17,613 of 35,345 by trust here, #31,361 by citations
• Computer Networks and Communications — 49 citations into it; #871 of 9,886 by trust here, #3,993 by citations
• Reinforcement Learning — 43 citations into it
• Optimization & Training — 33 citations into it
• Electrical and Electronic Engineering — 27 citations into it
• Management Science and Operations Research — 20 citations into it
• Computational Mechanics — 18 citations into it; #1,208 of 10,001 by trust here, #4,039 by citations
• Control and Systems Engineering — 17 citations into it

Whom Guannan Qu trusts overall

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

1. Angelia Nedić
2. John N. Tsitsiklis
3. Alan M. Frieze
4. Alexander Olshevsky
5. Yurii E. Nesterov
6. Wei Shi
7. Sébastien Bubeck
8. Dimitri P. Bertsekas
9. Asuman E. Ozdaglar
10. Dusan Jakovetic

Who trusts Guannan Qu overall

1. Angelia Nedić
2. Thinh T. Doan
3. Alexander Olshevsky
4. Shi Pu
5. Jinshan Zeng
6. Afshin Oroojlooyjadid
7. Fakhteh Saadatniaki
8. Alireza Fallah
9. César A. Uribe
10. Amirhossein Reisizadeh

Papers from Guannan Qu's perspective

Ordered by how strongly the researchers Guannan Qu 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. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Reinforcement Learning: An Introduction
10. Human-level control through deep reinforcement learning

Their papers

• Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges (2021) — 383 citations
• Markov Decision Processes with Time-varying Transition Probabilities and Rewards (2019)
• Accelerated Distributed Nesterov Gradient Descent (2017) — 263 citations
• Harnessing smoothness to accelerate distributed optimization (2016) — 658 citations