Feature Importance Measure for Non-linear Learning Algorithms

22 November 2016

Papers citing "Feature Importance Measure for Non-linear Learning Algorithms"

8 / 8 papers shown

Title
DORA: Exploring Outlier Representations in Deep Neural Networks Kirill Bykov Mayukh Deb Dennis Grinwald Klaus-Robert Muller Marina M.-C. Höhne 27 12 0 09 Jun 2022
This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation Srishti Gautam Marina M.-C. Höhne Stine Hansen Robert Jenssen Michael C. Kampffmeyer 27 49 0 27 Aug 2021
Explaining Bayesian Neural Networks Kirill Bykov Marina M.-C. Höhne Adelaida Creosteanu Klaus-Robert Muller Frederick Klauschen Shinichi Nakajima Marius Kloft BDL AAML 36 25 0 23 Aug 2021
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks Kirill Bykov Marina M.-C. Höhne Klaus-Robert Muller Shinichi Nakajima Marius Kloft UQCV FAtt 29 31 0 16 Jun 2020
Post-hoc explanation of black-box classifiers using confident itemsets M. Moradi Matthias Samwald 57 98 0 05 May 2020
TSInsight: A local-global attribution framework for interpretability in time-series data Shoaib Ahmed Siddiqui Dominique Mercier Andreas Dengel Sheraz Ahmed FAtt AI4TS 16 12 0 06 Apr 2020
ACE -- An Anomaly Contribution Explainer for Cyber-Security Applications Xiao Zhang Manish Marwah I-Ta Lee M. Arlitt Dan Goldwasser 21 14 0 01 Dec 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017