Ming Yin

Artificial Intelligence · with Vaughan · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 54 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it; #13,327 of 35,345 by trust here, #19,018 by citations
• Safety, Robustness & Interpretability — 24 citations into it
• Safety Research — 12 citations into it; #1,350 of 5,829 by trust here, #2,429 by citations
• Management Science and Operations Research — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it
• General Decision Sciences — 5 citations into it
• Human-Computer Interaction — 5 citations into it
• Natural Language Processing — 5 citations into it

Whom Ming Yin trusts overall

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

1. Zachary Chase Lipton
2. Finale Doshi-Velez
3. Tim Miller
4. Geoff Cumming
5. Pierre Dragicevic
6. Been Kim
7. R. Caruana
8. Marco Tulio Ribeiro
9. Raymond J. Fisman
10. Geoffrey E. Hinton

Who trusts Ming Yin overall

1. Q. Liao
2. Vivian Lai
3. Xinru Wang
4. Zana Buçinca
5. Yunfeng Zhang
6. Tanya Nazaretsky
7. Chacha Chen
8. Alison Smith-Renner
9. Han Liu
10. Phoebe Lin

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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Distilling the Knowledge in a Neural Network (arXiv.org)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Understanding the Effect of Accuracy on Trust in Machine Learning Models (2019) — 599 citations
• Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making (2021) — 418 citations