Interpretation of Neural Networks is Fragile

29 October 2017

Papers citing "Interpretation of Neural Networks is Fragile"

50 / 467 papers shown

Title
Encoding Concepts in Graph Neural Networks Lucie Charlotte Magister Pietro Barbiero Dmitry Kazhdan F. Siciliano Gabriele Ciravegna Fabrizio Silvestri M. Jamnik Pietro Lio' 30 21 0 27 Jul 2022
Equivariant and Invariant Grounding for Video Question Answering Yicong Li Xiang Wang Junbin Xiao Tat-Seng Chua 18 25 0 26 Jul 2022
Calibrate to Interpret Gregory Scafarto N. Posocco Antoine Bonnefoy FaML 11 3 0 07 Jul 2022
Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions Zulqarnain Khan Davin Hill A. Masoomi Joshua Bone Jennifer Dy AAML 41 3 0 24 Jun 2022
Robustness of Explanation Methods for NLP Models Shriya Atmakuri Tejas Chheda Dinesh Kandula Nishant Yadav Taesung Lee Hessel Tuinhof FAtt AAML 19 4 0 24 Jun 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 26 140 0 22 Jun 2022
Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models Emily Black Hadi Elzayn Alexandra Chouldechova Jacob Goldin Daniel E. Ho MLAU 25 25 0 20 Jun 2022
Efficiently Training Low-Curvature Neural Networks Suraj Srinivas Kyle Matoba Himabindu Lakkaraju F. Fleuret AAML 23 15 0 14 Jun 2022
On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective M. Serrurier Franck Mamalet Thomas Fel Louis Bethune Thibaut Boissin AAML FAtt 32 4 0 14 Jun 2022
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure Paul Novello Thomas Fel David Vigouroux FAtt 14 27 0 13 Jun 2022
Xplique: A Deep Learning Explainability Toolbox Thomas Fel Lucas Hervier David Vigouroux Antonin Poché Justin Plakoo ... Agustin Picard C. Nicodeme Laurent Gardes G. Flandin Thomas Serre 11 30 0 09 Jun 2022
Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark Mohamed Karim Belaid Eyke Hüllermeier Maximilian Rabus Ralf Krestel ELM 16 0 0 08 Jun 2022
Fooling Explanations in Text Classifiers Adam Ivankay Ivan Girardi Chiara Marchiori P. Frossard AAML 22 20 0 07 Jun 2022
Saliency Cards: A Framework to Characterize and Compare Saliency Methods Angie Boggust Harini Suresh Hendrik Strobelt John Guttag Arvindmani Satyanarayan FAtt XAI 30 8 0 07 Jun 2022
A Human-Centric Take on Model Monitoring Murtuza N. Shergadwala Himabindu Lakkaraju K. Kenthapadi 37 9 0 06 Jun 2022
Use-Case-Grounded Simulations for Explanation Evaluation Valerie Chen Nari Johnson Nicholay Topin Gregory Plumb Ameet Talwalkar FAtt ELM 22 24 0 05 Jun 2022
Interpretable Mixture of Experts Aya Abdelsalam Ismail Sercan Ö. Arik Jinsung Yoon Ankur Taly S. Feizi Tomas Pfister MoE 20 10 0 05 Jun 2022
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations Tessa Han Suraj Srinivas Himabindu Lakkaraju FAtt 32 87 0 02 Jun 2022
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 44 18 0 31 May 2022
Scalable Interpretability via Polynomials Abhimanyu Dubey Filip Radenovic D. Mahajan 4 30 0 27 May 2022
Towards a Theory of Faithfulness: Faithful Explanations of Differentiable Classifiers over Continuous Data Nico Potyka Xiang Yin Francesca Toni FAtt 11 2 0 19 May 2022
The Solvability of Interpretability Evaluation Metrics Yilun Zhou J. Shah 68 8 0 18 May 2022
Sparse Visual Counterfactual Explanations in Image Space Valentyn Boreiko Maximilian Augustin Francesco Croce Philipp Berens Matthias Hein BDL CML 30 26 0 16 May 2022
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection Fan Wang A. Kong 63 10 0 15 May 2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations Jessica Dai Sohini Upadhyay Ulrich Aivodji Stephen H. Bach Himabindu Lakkaraju 40 56 0 15 May 2022
Explainable Deep Learning Methods in Medical Image Classification: A Survey Cristiano Patrício João C. Neves Luís F. Teixeira XAI 24 52 0 10 May 2022
Should attention be all we need? The epistemic and ethical implications of unification in machine learning N. Fishman Leif Hancox-Li 25 10 0 09 May 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 38 77 0 06 May 2022
ExSum: From Local Explanations to Model Understanding Yilun Zhou Marco Tulio Ribeiro J. Shah FAtt LRM 11 25 0 30 Apr 2022
Poly-CAM: High resolution class activation map for convolutional neural networks A. Englebert O. Cornu Christophe De Vleeschouwer 22 10 0 28 Apr 2022
It Takes Two Flints to Make a Fire: Multitask Learning of Neural Relation and Explanation Classifiers Zheng Tang Mihai Surdeanu 19 6 0 25 Apr 2022
Backdooring Explainable Machine Learning Maximilian Noppel Lukas Peter Christian Wressnegger AAML 16 5 0 20 Apr 2022
A Survey and Perspective on Artificial Intelligence for Security-Aware Electronic Design Automation D. Koblah R. Acharya Daniel Capecci Olivia P. Dizon-Paradis Shahin Tajik F. Ganji D. Woodard Domenic Forte 20 12 0 19 Apr 2022
Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention Yu Yang Seung Wook Kim Jungseock Joo FAtt 11 17 0 10 Apr 2022
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models Alexander Stevens Johannes De Smedt XAI FaML 12 12 0 30 Mar 2022
Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms H. Ragnarsdóttir Laura Manduchi H. Michel F. Laumer S. Wellmann Ece Ozkan Julia-Franziska Vogt 13 3 0 24 Mar 2022
Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation Hanjie Chen Yangfeng Ji OOD AAML VLM 24 21 0 23 Mar 2022
Rethinking Stability for Attribution-based Explanations Chirag Agarwal Nari Johnson Martin Pawelczyk Satyapriya Krishna Eshika Saxena Marinka Zitnik Himabindu Lakkaraju FAtt 22 50 0 14 Mar 2022
Explaining Classifiers by Constructing Familiar Concepts Johannes Schneider M. Vlachos 27 15 0 07 Mar 2022
Concept-based Explanations for Out-Of-Distribution Detectors Jihye Choi Jayaram Raghuram Ryan Feng Jiefeng Chen S. Jha Atul Prakash OODD 19 12 0 04 Mar 2022
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths Amir Hossein Akhavan Rahnama Judith Butepage XAI FAtt 11 0 0 04 Mar 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 21 22 0 22 Feb 2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis Thomas Fel Mélanie Ducoffe David Vigouroux Rémi Cadène Mikael Capelle C. Nicodeme Thomas Serre AAML 23 41 0 15 Feb 2022
Rethinking Explainability as a Dialogue: A Practitioner's Perspective Himabindu Lakkaraju Dylan Slack Yuxin Chen Chenhao Tan Sameer Singh LRM 16 64 0 03 Feb 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 177 186 0 03 Feb 2022
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 19 1 0 30 Jan 2022
Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning Amit Dhurandhar K. Ramamurthy Kartik Ahuja Vijay Arya FAtt 12 4 0 28 Jan 2022
Diagnosing AI Explanation Methods with Folk Concepts of Behavior Alon Jacovi Jasmijn Bastings Sebastian Gehrmann Yoav Goldberg Katja Filippova 36 15 0 27 Jan 2022
A Comprehensive Study of Image Classification Model Sensitivity to Foregrounds, Backgrounds, and Visual Attributes Mazda Moayeri Phillip E. Pope Yogesh Balaji S. Feizi VLM 33 52 0 26 Jan 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 28 395 0 20 Jan 2022