Jie Wang

Reinforcement Learning · with Chu · 2016–2019

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 19 citations into it
• Artificial Intelligence — 9 citations into it
• Control and Systems Engineering — 8 citations into it

Whom Jie Wang trusts overall

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

1. Marco A. Wiering
2. Ronald J. Williams
3. Csaba Szepesvari
4. Richard S. Sutton
5. Shalabh Bhatnagar
6. van der D David Pol
7. Volodymyr Mnih
8. John N. Tsitsiklis
9. Carlos Guestrin
10. Christopher J. C. H. Watkins

Who trusts Jie Wang overall

1. Hua Wei
2. Saeed Rahmani

Papers from Jie Wang's perspective

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

1. Reinforcement Learning: An Introduction
2. Long Short-Term Memory
3. Gradient-based learning applied to document recognition
4. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
5. Human-level control through deep reinforcement learning
6. Asynchronous Methods for Deep Reinforcement Learning
7. ImageNet classification with deep convolutional neural networks
8. Policy Gradient Methods for Reinforcement Learning with Function Approximation
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control (2019) — 870 citations
• An adaptive online learning framework for practical breast cancer diagnosis (2016) — 7 citations