Emily Jia

Safety, Robustness & Interpretability · 2019

A researcher in the Science corpus: 2 papers; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Safety, Robustness & Interpretability — 27 citations into it
• Artificial Intelligence — 25 citations into it

Whom Emily Jia trusts overall

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

1. Marco Tulio Ribeiro
2. Scott M. Lundberg
3. Been Kim

Papers from Emily Jia's perspective

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

1. The mythos of model interpretability
2. Association for Computational Linguistics " Why Should I Trust You? " Explaining the Predictions of Any Classifier
3. Towards A Rigorous Science of Interpretable Machine Learning
4. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
5. Anchors: High-Precision Model-Agnostic Explanations
6. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
7. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
8. Interpretation of Neural Networks is Fragile
9. ImageNet classification with deep convolutional neural networks
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods (2019) — 1,143 citations
• How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods (2019) — 70 citations