Provably Better Explanations with Optimized Aggregation of Feature Attributions

7 June 2024

Jindong Gu

Papers citing "Provably Better Explanations with Optimized Aggregation of Feature Attributions"

37 / 37 papers shown

Title
Does Your Model Think Like an Engineer? Explainable AI for Bearing Fault Detection with Deep Learning Thomas Decker Michael Lebacher Volker Tresp 18 12 0 19 Oct 2023
Impossibility Theorems for Feature Attribution Blair Bilodeau Natasha Jaques Pang Wei Koh Been Kim FAtt 54 75 0 22 Dec 2022
OpenXAI: Towards a Transparent Evaluation of Model Explanations Chirag Agarwal Dan Ley Satyapriya Krishna Eshika Saxena Martin Pawelczyk Nari Johnson Isha Puri Marinka Zitnik Himabindu Lakkaraju XAI 69 145 0 22 Jun 2022
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations Tessa Han Suraj Srinivas Himabindu Lakkaraju FAtt 85 86 0 02 Jun 2022
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 100 19 0 31 May 2022
Rethinking Stability for Attribution-based Explanations Chirag Agarwal Nari Johnson Martin Pawelczyk Satyapriya Krishna Eshika Saxena Marinka Zitnik Himabindu Lakkaraju FAtt 68 51 0 14 Mar 2022
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond Anna Hedström Leander Weber Dilyara Bareeva Daniel G. Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne XAI ELM 44 175 0 14 Feb 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Meike Nauta Jan Trienes Shreyasi Pathak Elisa Nguyen Michelle Peters Yasmin Schmitt Jorg Schlotterer M. V. Keulen C. Seifert ELM XAI 89 409 0 20 Jan 2022
NoiseGrad: Enhancing Explanations by Introducing Stochasticity to Model Weights Kirill Bykov Anna Hedström Shinichi Nakajima Marina M.-C. Höhne FAtt 48 34 0 18 Jun 2021
MLP-Mixer: An all-MLP Architecture for Vision Ilya O. Tolstikhin N. Houlsby Alexander Kolesnikov Lucas Beyer Xiaohua Zhai ... Andreas Steiner Daniel Keysers Jakob Uszkoreit Mario Lucic Alexey Dosovitskiy 418 2,674 0 04 May 2021
Do Feature Attribution Methods Correctly Attribute Features? Yilun Zhou Serena Booth Marco Tulio Ribeiro J. Shah FAtt XAI 80 135 0 27 Apr 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations N. Jethani Mukund Sudarshan Yindalon Aphinyanagphongs Rajesh Ranganath FAtt 127 70 0 02 Mar 2021
What does LIME really see in images? Damien Garreau Dina Mardaoui FAtt 56 38 0 11 Feb 2021
Training data-efficient image transformers & distillation through attention Hugo Touvron Matthieu Cord Matthijs Douze Francisco Massa Alexandre Sablayrolles Hervé Jégou ViT 384 6,768 0 23 Dec 2020
Debugging Tests for Model Explanations Julius Adebayo M. Muelly Ilaria Liccardi Been Kim FAtt 64 181 0 10 Nov 2020
Captum: A unified and generic model interpretability library for PyTorch Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang B. Alsallakh ... Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan Orion Reblitz-Richardson FAtt 133 841 0 16 Sep 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 87 224 0 01 May 2020
There and Back Again: Revisiting Backpropagation Saliency Methods Sylvestre-Alvise Rebuffi Ruth C. Fong Xu Ji Andrea Vedaldi FAtt XAI 66 113 0 06 Apr 2020
When Explanations Lie: Why Many Modified BP Attributions Fail Leon Sixt Maximilian Granz Tim Landgraf BDL FAtt XAI 52 132 0 20 Dec 2019
Understanding Deep Networks via Extremal Perturbations and Smooth Masks Ruth C. Fong Mandela Patrick Andrea Vedaldi AAML 71 416 0 18 Oct 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 81 332 0 19 Jun 2019
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 134 1,967 0 08 Oct 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 108 681 0 28 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 76 526 0 21 Jun 2018
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations Weili Nie Yang Zhang Ankit B. Patel FAtt 122 151 0 18 May 2018
MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler Andrew G. Howard Menglong Zhu A. Zhmoginov Liang-Chieh Chen 181 19,284 0 13 Jan 2018
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 98 685 0 02 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,906 0 22 May 2017
Real Time Image Saliency for Black Box Classifiers P. Dabkowski Y. Gal 67 589 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 198 3,873 0 10 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 5,989 0 04 Mar 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 297 20,023 0 07 Oct 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 772 36,813 0 25 Aug 2016
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Shcherbina A. Kundaje FAtt 82 788 0 05 May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,672 0 21 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,295 0 20 Dec 2013