Carolyn Kim

Artificial Intelligence · 5 papers · 2017–2025

A researcher in the Science corpus: 5 papers; trusts 66 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it; #7,144 of 35,345 by trust here, #11,624 by citations
• Safety, Robustness & Interpretability — 34 citations into it; #558 of 2,251 by trust here, #1,078 by citations
• Statistics and Probability — 14 citations into it
• Medical & Health AI — 12 citations into it
• Statistical Learning Theory — 10 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Information Systems — 9 citations into it
• Causal Inference & Discovery — 8 citations into it

Whom Carolyn Kim trusts overall

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

1. Leo Breiman
2. Susan Athey
3. Jennifer L. Hill
4. Marco Tulio Ribeiro
5. R. Caruana
6. Cynthia Rudin
7. Robert Tibshirani
8. Himabindu Lakkaraju
9. Peter L. Bartlett
10. Fredrik D. Johansson

Who trusts Carolyn Kim overall

1. Matthew R. O'Shaughnessy
2. Amit Dhurandhar
3. Patrick Hall
4. Osbert Bastani
5. Mengnan Du
6. Yacine Izza
7. Amina Adadi
8. Piyawat Lertvittayakumjorn
9. Vaishak Belle
10. Stefano Teso

Papers from Carolyn Kim's perspective

Ordered by how strongly the researchers Carolyn Kim 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Interpretable Models with Causal Guarantees (2019) — 19 citations
• Simulating 500 million years of evolution with a language model. (2025) — 309 citations
• Simulating 500 million years of evolution with a language model (2024) — 677 citations
• Interpreting Blackbox Models via Model Extraction (2017) — 196 citations
• Interpretability via Model Extraction (2017) — 139 citations