Linglong Kong

Artificial Intelligence · 2008–2019

A researcher in the Science corpus: 4 papers; trusts 40 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it
• Reinforcement Learning — 49 citations into it
• Management Science and Operations Research — 20 citations into it
• Statistics and Probability — 17 citations into it; #11,077 of 12,932 by trust here, #9,714 by citations
• Computer Networks and Communications — 12 citations into it
• Economics and Econometrics — 7 citations into it
• Safety, Robustness & Interpretability — 7 citations into it
• Control and Systems Engineering — 5 citations into it

Whom Linglong Kong trusts overall

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

1. Richard S. Sutton
2. Bastian Goldlücke
3. Nicole A. Lazar
4. Ying Wei
5. Marc G. Bellemare
6. Peter Auer
7. Csaba Szepesvari
8. Andrew Barto
9. Cynthia Dwork
10. Volodymyr Mnih

Who trusts Linglong Kong overall

1. Robert Serfling
2. Leying Guan
3. Anirvan Chakraborty
4. Guillaume Carlier

Papers from Linglong Kong's perspective

Ordered by how strongly the researchers Linglong Kong 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. Reinforcement Learning: An Introduction
6. Asynchronous Methods for Deep Reinforcement Learning
7. Human-level control through deep reinforcement learning
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Quantile tomography: using quantiles with multivariate data (2008) — 159 citations
• QUOTA: The Quantile Option Architecture for Reinforcement Learning (2018) — 33 citations
• Learning Privately over Distributed Features: An ADMM Sharing Approach (2019) — 39 citations
• Distributional Reinforcement Learning for Efficient Exploration (2019) — 107 citations