Ming Yin

Artificial Intelligence · with Sun · 2021–2023

A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 122 citations into it
• Reinforcement Learning — 24 citations into it
• Management Science and Operations Research — 16 citations into it
• Statistical Learning Theory — 6 citations into it
• Computer Networks and Communications — 5 citations into it
• Statistics and Probability — 5 citations into it

Whom Ming Yin trusts overall

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

1. Nan Jiang
2. R´emi Munos
3. Tengyang Xie
4. Richard S. Sutton
5. Lin F. Yang
6. Rie Johnson
7. Geoffrey E. Hinton
8. András Antos
9. Jinglin Chen
10. Lihong Li

Who trusts Ming Yin overall

1. Michael Janner

Papers from Ming Yin's perspective

Ordered by how strongly the researchers Ming Yin 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• MMMU: A Massive Multi-Discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI (2023) — 2,391 citations
• TheoremQA: A Theorem-driven Question Answering dataset (2023) — 250 citations
• Near-Optimal Offline Reinforcement Learning via Double Variance Reduction (2021) — 71 citations