Extracting Rules from Deep Neural Networks

By P. Glöckner, Eneldo Loza Mencía. 2015

Subfields: Safety, Robustness & Interpretability

How researchers see this paper

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

In Safety, Robustness & Interpretability

1. Leilani H. Gilpin
2. Ben Z. Yuan
3. David Bau
4. Michael A. Specter
5. Yoichi Hayashi
6. Lalana Kagal
7. Guido Bologna
8. Ayesha Bajwa

Citations in the corpus

• Explaining Explanations: An Overview of Interpretability of Machine Learning (2018, background): “Another useful survey can be found in the master’s thesis by Zilke [27].”
• Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning (2017, methodology): “Zilke introduced a rule extraction algorithm that was applied to deep MLPs [17] by extending an already existing decompositional method [18].”
• Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning (2018, background): “Another useful survey can be found in the master’s thesis by Zilke [27].”