Ying Wen

Artificial Intelligence · Computer Vision and Pattern Recognition · 2016–2023

A researcher in the Science corpus: 12 papers; trusts 200 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 209 citations into it; #20,075 of 35,345 by trust here, #6,956 by citations
• Reinforcement Learning — 177 citations into it; #1,830 of 2,242 by trust here, #700 by citations
• Computer Vision and Pattern Recognition — 69 citations into it
• Game & Decision Theory — 36 citations into it
• Management Science and Operations Research — 35 citations into it
• Natural Language Processing — 31 citations into it
• Optimization & Training — 20 citations into it
• LLM Agents & Tool Use — 19 citations into it

Whom Ying Wen trusts overall

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

1. Geoffrey E. Hinton
2. Richard S. Sutton
3. Michael L. Littman
4. Alex Krizhevsky
5. Kaiming He
6. Yoshua Bengio
7. Diederik P. Kingma
8. Ilya Sutskever
9. Sergey Levine
10. Volodymyr Mnih

Who trusts Ying Wen overall

1. Tonghan Wang
2. Jakub Grudzien Kuba
3. Jiechuan Jiang
4. Muning Wen
5. Yihan Wang
6. Tabish Rashid
7. Yaodong Yang
8. Marie Ossenkopf
9. Jakob N. Foerster
10. Alex Peysakhovich

Papers from Ying Wen's perspective

Ordered by how strongly the researchers Ying 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. 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training (2023) — 351 citations
• Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games (2017) — 373 citations
• Multi-Agent Reinforcement Learning is a Sequence Modeling Problem (2022) — 362 citations
• An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning (2017) — 7 citations
• Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning (2019) — 169 citations
• Factorized Q-learning for large-scale multi-agent systems (2018) — 75 citations
• Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning (2021) — 407 citations
• A Study of AI Population Dynamics with Million-agent Reinforcement Learning (2017) — 41 citations
• SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy (2023) — 596 citations
• Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games (2021) — 20 citations
• Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games (2017) — 216 citations
• Product-Based Neural Networks for User Response Prediction (2016) — 790 citations