Considerations When Learning Additive Explanations for Black-Box Models

26 January 2018

Papers citing "Considerations When Learning Additive Explanations for Black-Box Models"

6 / 6 papers shown

Title
Disentangling Visual Transformers: Patch-level Interpretability for Image Classification Guillaume Jeanneret Loïc Simon F. Jurie ViT 49 0 0 24 Feb 2025
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory Fabian Fumagalli Maximilian Muschalik Eyke Hüllermeier Barbara Hammer J. Herbinger FAtt 39 1 0 22 Dec 2024
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients Evandro S. Ortigossa Fábio F. Dias Brian Barr Claudio T. Silva L. G. Nonato FAtt 56 2 0 25 Apr 2024
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations Sérgio Jesus Catarina Belém Vladimir Balayan João Bento Pedro Saleiro P. Bizarro João Gama 136 119 0 21 Jan 2021
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 48 195 0 06 Apr 2017