Qinghua Liu

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 4 papers; trusts 73 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 111 citations into it
• Reinforcement Learning — 55 citations into it
• Management Science and Operations Research — 41 citations into it
• Optimization & Training — 34 citations into it
• Federated & Distributed Learning — 22 citations into it
• Statistical Learning Theory — 20 citations into it
• Computer Vision and Pattern Recognition — 17 citations into it
• ML Systems & Hardware — 10 citations into it

Whom Qinghua Liu trusts overall

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

1. Chi Jin
2. Michael L. Littman
3. Alex Krizhevsky
4. Richard S. Sutton
5. Geoffrey E. Hinton
6. Mohammad Gheshlaghi Azar
7. Csaba Szepesvari
8. Michael Kearns
9. Ronen I. Brafman
10. Ian Osband

Who trusts Qinghua Liu overall

1. Young Geun Kim
2. Yae Jee Cho
3. Khaled Ben Letaief
4. Hangyu Zhu

Papers from Qinghua Liu's perspective

Ordered by how strongly the researchers Qinghua Liu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms (2021) — 261 citations
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization (2020) — 2,059 citations
• Provable Rich Observation Reinforcement Learning with Combinatorial Latent States (2021) — 8 citations
• A Sharp Analysis of Model-based Reinforcement Learning with Self-Play (2020) — 141 citations