Yin Lou

Artificial Intelligence · 2009–2018

A researcher in the Science corpus: 9 papers; trusts 78 researchers and is trusted by 102 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 94 citations into it; #2,457 of 35,345 by trust here, #2,484 by citations
• Statistics and Probability — 70 citations into it; #7,921 of 12,932 by trust here, #9,233 by citations
• Safety, Robustness & Interpretability — 34 citations into it; #223 of 2,251 by trust here, #443 by citations
• Computer Vision and Pattern Recognition — 33 citations into it; #4,300 of 21,575 by trust here, #6,089 by citations
• Signal Processing — 32 citations into it; #6,297 of 7,572 by trust here, #4,560 by citations
• AI Ethics, Fairness & Society — 22 citations into it
• Safety Research — 20 citations into it; #2,583 of 5,829 by trust here, #2,344 by citations
• Statistical Learning Theory — 17 citations into it

Whom Yin Lou trusts overall

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

1. Jerome H. Friedman
2. Giles Hooker
3. Alan Y. Chiang
4. Robert Tibshirani
5. Trevor J. Hastie
6. Eric R. Ziegel
7. Leo Breiman
8. Simon N. Wood
9. R. Caruana
10. Pradeep Ravikumar

Who trusts Yin Lou overall

1. Sarah Tan
2. R. Caruana
3. Cynthia Rudin
4. Daniel S. Weld
5. Giles Hooker
6. Yu Zheng
7. Giulia Vilone
8. Erico Tjoa
9. Riccardo Guidotti
10. Xuan Liu

Papers from Yin Lou's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission (2015) — 1,942 citations
• MatchMiner: Efficient Spanning Structure Mining in Large Image Collections (2012) — 51 citations
• Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation (2017) — 212 citations
• Intelligible models for classification and regression (2012) — 575 citations
• Sparse Partially Linear Additive Models (2014) — 60 citations
• Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models (2018) — 3 citations
• Auditing Black-Box Models Using Transparent Model Distillation With Side Information (2017) — 18 citations
• Accurate intelligible models with pairwise interactions (2013) — 703 citations
• Map-matching for low-sampling-rate GPS trajectories (2009) — 886 citations