On the (In)fidelity and Sensitivity for Explanations

27 January 2019

David I. Inouye

Papers citing "On the (In)fidelity and Sensitivity for Explanations"

34 / 84 papers shown

Title
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 21 168 0 14 Feb 2022
Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods Dominique Mercier Jwalin Bhatt Andreas Dengel Sheraz Ahmed AI4TS 22 11 0 08 Feb 2022
Towards a consistent interpretation of AIOps models Yingzhe Lyu Gopi Krishnan Rajbahadur Dayi Lin Boyuan Chen Zhen Ming Z. Jiang AI4CE 22 19 0 04 Feb 2022
Topological Representations of Local Explanations Peter Xenopoulos G. Chan Harish Doraiswamy L. G. Nonato Brian Barr Claudio Silva FAtt 25 4 0 06 Jan 2022
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View Di Jin Elena Sergeeva W. Weng Geeticka Chauhan Peter Szolovits OOD 31 55 0 05 Dec 2021
Defense Against Explanation Manipulation Ruixiang Tang Ninghao Liu Fan Yang Na Zou Xia Hu AAML 44 11 0 08 Nov 2021
Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning Bang Xiang Yong Alexandra Brintrup 21 6 0 19 Oct 2021
TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models S. Chatterjee Arnab Das Chirag Mandal Budhaditya Mukhopadhyay Manish Vipinraj Aniruddh Shukla R. Rao Chompunuch Sarasaen Oliver Speck A. Nürnberger MedIm 37 14 0 16 Oct 2021
Diagnostics-Guided Explanation Generation Pepa Atanasova J. Simonsen Christina Lioma Isabelle Augenstein LRM FAtt 38 6 0 08 Sep 2021
A Survey on Automated Fact-Checking Zhijiang Guo M. Schlichtkrull Andreas Vlachos 27 457 0 26 Aug 2021
Semantic Concentration for Domain Adaptation Shuang Li Mixue Xie Fangrui Lv Chi Harold Liu Jian Liang C. Qin Wei Li 52 87 0 12 Aug 2021
Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff Michael J. Naylor C. French Samantha R. Terker Uday Kamath 36 10 0 12 Jul 2021
What will it take to generate fairness-preserving explanations? Jessica Dai Sohini Upadhyay Stephen H. Bach Himabindu Lakkaraju FAtt FaML 13 14 0 24 Jun 2021
On Locality of Local Explanation Models Sahra Ghalebikesabi Lucile Ter-Minassian Karla Diaz-Ordaz Chris Holmes FedML FAtt 26 39 0 24 Jun 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White W. Neiswanger 37 65 0 23 Jun 2021
On the Sensitivity and Stability of Model Interpretations in NLP Fan Yin Zhouxing Shi Cho-Jui Hsieh Kai-Wei Chang FAtt 16 33 0 18 Apr 2021
Shapley Explanation Networks Rui Wang Xiaoqian Wang David I. Inouye TDI FAtt 21 44 0 06 Apr 2021
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing Ioannis Kakogeorgiou Konstantinos Karantzalos XAI 23 118 0 03 Apr 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 35 25 0 20 Mar 2021
EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry Yingqi Liu Guangyu Shen Guanhong Tao Zhenting Wang Shiqing Ma Xinming Zhang AAML 30 8 0 16 Mar 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 21 57 0 25 Feb 2021
Understanding Failures of Deep Networks via Robust Feature Extraction Sahil Singla Besmira Nushi S. Shah Ece Kamar Eric Horvitz FAtt 28 83 0 03 Dec 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 29 93 0 22 Sep 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 29 821 0 16 Sep 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 27 37 0 13 Jul 2020
Proper Network Interpretability Helps Adversarial Robustness in Classification Akhilan Boopathy Sijia Liu Gaoyuan Zhang Cynthia Liu Pin-Yu Chen Shiyu Chang Luca Daniel AAML FAtt 21 66 0 26 Jun 2020
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei-Yue Wang AAML 46 18 0 09 Jun 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 33 218 0 01 May 2020
Model Agnostic Multilevel Explanations K. Ramamurthy B. Vinzamuri Yunfeng Zhang Amit Dhurandhar 21 41 0 12 Mar 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 30 143 0 10 Feb 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan Ö. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 27 66 0 16 Jul 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 22 101 0 08 Jun 2019
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017