Bei Peng

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 10 papers; trusts 138 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 268 citations into it; #34,484 of 35,345 by trust here, #8,439 by citations
• Reinforcement Learning — 243 citations into it; #1,694 of 2,242 by trust here, #819 by citations
• Computational Theory and Mathematics — 46 citations into it
• Control and Systems Engineering — 41 citations into it
• Game & Decision Theory — 36 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Management Science and Operations Research — 28 citations into it
• Sociology and Political Science — 27 citations into it

Whom Bei Peng trusts overall

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

1. Richard S. Sutton
2. Tabish Rashid
3. Geoffrey E. Hinton
4. Peter Stone
5. Jakob N. Foerster
6. Yoshua Bengio
7. Andrew Barto
8. Diederik P. Kingma
9. J. Urgen Schmidhuber
10. Mikayel Samvelyan

Who trusts Bei Peng overall

1. Chao Yu
2. Ruohan Zhang
3. Christian Schröder de Witt
4. Xiaoteng Ma
5. Chenghao Li
6. Jakub Grudzien Kuba
7. Xin Wang
8. Pararth Shah

Papers from Bei Peng's perspective

Ordered by how strongly the researchers Bei Peng 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Reinforcement Learning: An Introduction
9. GENERATIVE ADVERSARIAL NETS
10. Human-level control through deep reinforcement learning

Their papers

• Interactive Learning from Policy-Dependent Human Feedback (2017) — 346 citations
• Learning behaviors via human-delivered discrete feedback: modeling implicit feedback strategies to speed up learning (2015) — 109 citations
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey (2020) — 748 citations
• RODE: Learning Roles to Decompose Multi-Agent Tasks (2020) — 286 citations
• Weighted QMIX: Expanding Monotonic Value Function Factorisation (2020) — 490 citations
• FACMAC: Factored Multi-Agent Centralised Policy Gradients (2020) — 370 citations
• Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning (2020) — 88 citations
• AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning (2020) — 23 citations
• Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control (2020) — 100 citations
• Learning something from nothing: Leveraging implicit human feedback strategies (2014) — 31 citations