Minimizing False-Positive Attributions in Explanations of Non-Linear Models

16 May 2025

Papers citing "Minimizing False-Positive Attributions in Explanations of Non-Linear Models"

20 / 20 papers shown

Title
Explainable AI needs formal notions of explanation correctness Stefan Haufe Rick Wilming Benedict Clark Rustam Zhumagambetov Danny Panknin Ahcène Boubekki XAI 47 1 0 22 Sep 2024
GLIME: General, Stable and Local LIME Explanation Zeren Tan Yang Tian Jian Li FAtt LRM 46 19 0 27 Nov 2023
XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance Benedict Clark Rick Wilming Stefan Haufe 52 5 0 22 Jun 2023
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables Rick Wilming Leo Kieslich Benedict Clark Stefan Haufe 38 9 0 02 Jun 2023
XAI for Transformers: Better Explanations through Conservative Propagation Ameen Ali Thomas Schnake Oliver Eberle G. Montavon Klaus-Robert Muller Lior Wolf FAtt 39 92 0 15 Feb 2022
Scrutinizing XAI using linear ground-truth data with suppressor variables Rick Wilming Céline Budding K. Müller Stefan Haufe FAtt 31 26 0 14 Nov 2021
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L. Arras Ahmed Osman Wojciech Samek XAI AAML 54 153 0 16 Mar 2020
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 50 442 0 26 Sep 2019
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI Erico Tjoa Cuntai Guan XAI 78 1,427 0 17 Jul 2019
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 162 1,828 0 30 Nov 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 179 2,215 0 12 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 538 21,613 0 22 May 2017
Learning how to explain neural networks: PatternNet and PatternAttribution Pieter-Jan Kindermans Kristof T. Schütt Maximilian Alber K. Müller D. Erhan Been Kim Sven Dähne XAI FAtt 57 338 0 16 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 123 3,848 0 10 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 115 5,920 0 04 Mar 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 582 16,828 0 16 Feb 2016
Causal interpretation rules for encoding and decoding models in neuroimaging S. Weichwald Timm Meyer Ozan Özdenizci Bernhard Schölkopf T. Ball Moritz Grosse-Wentrup AI4CE CML 26 108 0 15 Nov 2015
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 174 4,653 0 21 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 177 7,252 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 321 15,825 0 12 Nov 2013