Emily Chen

Artificial Intelligence · with Doshi-Velez · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 21 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it
• Safety, Robustness & Interpretability — 45 citations into it
• Information Systems — 20 citations into it
• Statistical Learning Theory — 10 citations into it
• Natural Language Processing — 10 citations into it
• Cognitive Science & Psychology — 7 citations into it
• Algorithms & Complexity — 6 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Emily Chen trusts overall

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

1. Finale Doshi-Velez
2. William W. Cohen
3. Himabindu Lakkaraju
4. Niko Speybroeck
5. Been Kim
6. Tania Lombrozo
7. Marco Tulio Ribeiro
8. Cynthia Rudin
9. Peter Clark
10. Sameer Singh

Who trusts Emily Chen overall

1. Isaac Lage
2. Sina Mohseni
3. Vivian Lai
4. Xinru Wang
5. Wojciech Samek
6. Ben Green
7. Guang Yang
8. Hendrik Schuff

Papers from Emily Chen's perspective

Ordered by how strongly the researchers Emily Chen 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation (2018) — 262 citations
• An Evaluation of the Human-Interpretability of Explanation (2019) — 186 citations
• Human Evaluation of Models Built for Interpretability (2019) — 152 citations