Na Li
Electrical and Electronic Engineering · IEEE Transactions on Automatic Control · 2005–2021
A researcher in the Science corpus: 15 papers; trusts 70 researchers and is trusted by 57 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 77 citations into it; #2,556 of 16,539 by trust here, #6,291 by citations
- Artificial Intelligence — 75 citations into it; #8,894 of 35,345 by trust here, #20,836 by citations
- Computer Networks and Communications — 64 citations into it; #556 of 9,886 by trust here, #2,539 by citations
- Reinforcement Learning — 51 citations into it
- Control and Systems Engineering — 44 citations into it; #3,903 of 8,257 by trust here, #2,692 by citations
- Optimization & Training — 39 citations into it
- Management Science and Operations Research — 35 citations into it; #2,565 of 11,946 by trust here, #5,316 by citations
- Molecular Biology — 35 citations into it
Whom Na Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Angelia Nedić
- Dimitri P. Bertsekas
- John N. Tsitsiklis
- Alexander Olshevsky
- M. Solodov
- Alan M. Frieze
- Yurii E. Nesterov
- Steven H. Low
- Minghui Zhu
- Wei Shi
Who trusts Na Li overall
- Angelia Nedić
- Soomin Lee
- Steven H. Low
- Fahad Alismail
- Alexander Olshevsky
- Wenbo Shi
- Kai Man Tsui
- Lillian J. Ratliff
- Mohammad Rostami
- Maria Gorlatova
Papers from Na Li's perspective
Ordered by how strongly the researchers Na Li trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Reinforcement Learning: An Introduction
- Learning Multiple Layers of Features from Tiny Images
- Parallel and distributed computation
- Human-level control through deep reinforcement learning
- Asynchronous Methods for Deep Reinforcement Learning
Their papers
- Exact Convex Relaxation of Optimal Power Flow in Radial Networks (2013) — 533 citations
- The Genomes of Oryza sativa: A History of Duplications (2005) — 942 citations
- Two Market Models for Demand Response in Power Networks (2010) — 308 citations
- Designing games for distributed optimization (2011) — 316 citations
- Optimal demand response based on utility maximization in power networks (2011) — 999 citations
- Online Learning for Markov Decision Processes in Nonstationary Environments: A Dynamic Regret Analysis (2019) — 21 citations
- Connecting automatic generation control and economic dispatch from an optimization view (2014) — 377 citations
- Design and Stability of Load-Side Primary Frequency Control in Power Systems (2013) — 452 citations
- Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis (2019) — 115 citations
- Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2022 (2021) — 890 citations
- 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)
- Real-Time Energy Management in Microgrids (2017) — 318 citations
- Accelerated Distributed Nesterov Gradient Descent (2017) — 263 citations
- Harnessing smoothness to accelerate distributed optimization (2016) — 658 citations