Sarah Tan

Artificial Intelligence · with Song · 2019

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Sarah Tan trusts overall

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

  1. Sarah Tan

Papers from Sarah Tan's perspective

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

  1. The mythos of model interpretability
  2. Towards A Rigorous Science of Interpretable Machine Learning
  3. Anchors: High-Precision Model-Agnostic Explanations
  4. Learning Important Features Through Propagating Activation Differences
  5. Association for Computational Linguistics " Why Should I Trust You? " Explaining the Predictions of Any Classifier
  6. Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
  7. Interpretable Decision Sets: A Joint Framework for Description and Prediction
  8. Greedy function approximation: A gradient boosting machine.
  9. Axiomatic Attribution for Deep Networks
  10. Transparent Model Distillation

Their papers