NIPS Proceedingsβ

Been Kim

9 Papers

  • A Benchmark for Interpretability Methods in Deep Neural Networks (2019)
  • Towards Automatic Concept-based Explanations (2019)
  • Visualizing and Measuring the Geometry of BERT (2019)
  • Human-in-the-Loop Interpretability Prior (2018)
  • Sanity Checks for Saliency Maps (2018)
  • To Trust Or Not To Trust A Classifier (2018)
  • Examples are not enough, learn to criticize! Criticism for Interpretability (2016)
  • Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction (2015)
  • The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (2014)