Interpretable Machine Learning : Lessons from Topic Modeling

By Michael J. Paul. 2016

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Mohammed Berrada
2. Amina Adadi
3. Giulia Vilone
4. Gesina Schwalbe
5. Waddah Saeed
6. Xinru Wang
7. Meike Nauta
8. Gobinda Saha

Citations in the corpus

• Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “Paul ‘s claim [153] is based on the fact that a considerable number of methods have been proposed for improving and evaluating the interpretability of topic models and discusses how ideas from topic modeling such as human feedback and automated metrics could be applied to evaluating ML interpretabil”