Zheng Wen

Artificial Intelligence · with Roy · 2013–2020

A researcher in the Science corpus: 5 papers; trusts 57 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #4,970 of 35,345 by trust here, #7,728 by citations
• Reinforcement Learning — 82 citations into it; #490 of 2,242 by trust here, #773 by citations
• Management Science and Operations Research — 61 citations into it; #983 of 11,946 by trust here, #993 by citations
• Statistical Learning Theory — 16 citations into it
• Computer Networks and Communications — 15 citations into it; #8,447 of 9,886 by trust here, #7,492 by citations
• Statistics and Probability — 14 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Computational Theory and Mathematics — 9 citations into it

Whom Zheng Wen trusts overall

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

1. Richard S. Sutton
2. Michael Kearns
3. Ian Osband
4. Benjamin Van Roy
5. Csaba Szepesvari
6. Sham M. Kakade
7. Peter Auer
8. Andrew Barto
9. Satinder Singh
10. Bradley Efron

Who trusts Zheng Wen overall

1. Ian Osband
2. Simon Shaolei Du
3. Ruosong Wang
4. Benjamin Van Roy
5. Brendan O'Donoghue
6. Chi Jin
7. Lin F. Yang
8. Christoph Dann
9. Nan Jiang
10. Sham M. Kakade

Papers from Zheng Wen's perspective

Ordered by how strongly the researchers Zheng Wen 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Reinforcement Learning: An Introduction
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Human-level control through deep reinforcement learning
9. Attention is All you Need (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Generalization and Exploration via Randomized Value Functions (2014) — 339 citations
• Deep Exploration via Randomized Value Functions (2017) — 339 citations
• Cascading Bandits: Learning to Rank in the Cascade Model (2015) — 304 citations
• Hypermodels for Exploration (2020) — 49 citations
• Efficient Exploration and Value Function Generalization in Deterministic Systems (2013) — 76 citations