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1710.10547
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Interpretation of Neural Networks is Fragile
AAAI Conference on Artificial Intelligence (AAAI), 2017
29 October 2017
Amirata Ghorbani
Abubakar Abid
James Zou
FAtt
AAML
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Papers citing
"Interpretation of Neural Networks is Fragile"
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CRAFT: Concept Recursive Activation FacTorization for Explainability
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On the Robustness of Explanations of Deep Neural Network Models: A Survey
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262
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Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates
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06 Nov 2022
SoK: Modeling Explainability in Security Analytics for Interpretability, Trustworthiness, and Usability
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M. K. Veerabhadran
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286
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31 Oct 2022
BOREx: Bayesian-Optimization--Based Refinement of Saliency Map for Image- and Video-Classification Models
Asian Conference on Computer Vision (ACCV), 2022
Atsushi Kikuchi
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Kohei Suenaga
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214
1
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31 Oct 2022
Safety Verification for Neural Networks Based on Set-boundary Analysis
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Bai Xue
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221
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09 Oct 2022
Boundary-Aware Uncertainty for Feature Attribution Explainers
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Davin Hill
A. Masoomi
Max Torop
S. Ghimire
Jennifer Dy
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511
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Ensembling improves stability and power of feature selection for deep learning models
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Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge
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Towards Faithful Model Explanation in NLP: A Survey
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Chris Callison-Burch
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524
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Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
Neural Information Processing Systems (NeurIPS), 2022
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Gabriele Ciravegna
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Error Controlled Feature Selection for Ultrahigh Dimensional and Highly Correlated Feature Space Using Deep Learning
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397
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If Influence Functions are the Answer, Then What is the Question?
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Juhan Bae
Nathan Ng
Alston Lo
Marzyeh Ghassemi
Roger C. Grosse
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332
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12 Sep 2022
Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions
ACM Computing Surveys (ACM CSUR), 2022
Paul Pu Liang
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Louis-Philippe Morency
310
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"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution
Conference on Computer and Communications Security (CCS), 2022
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Yuhao Mao
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S. Ji
Yuwen Pu
Meng Han
Jianwei Yin
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156
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Generating detailed saliency maps using model-agnostic methods
Maciej Sakowicz
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154
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Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier
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Francesco Foscarin
Katharina Hoedt
Verena Praher
A. Flexer
Gerhard Widmer
221
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26 Aug 2022
SoK: Explainable Machine Learning for Computer Security Applications
European Symposium on Security and Privacy (Euro S&P), 2022
A. Nadeem
D. Vos
Clinton Cao
Luca Pajola
Simon Dieck
Robert Baumgartner
S. Verwer
362
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22 Aug 2022
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
IEEE International Conference on Computer Vision (ICCV), 2022
Wei Huang
Xingyu Zhao
Gao Jin
Xiaowei Huang
AAML
353
37
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19 Aug 2022
Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights
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Zhipu Zhou
Tarun Joshi
V. Nair
FAtt
214
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12 Aug 2022
Encoding Concepts in Graph Neural Networks
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Pietro Barbiero
Dmitry Kazhdan
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Pietro Lio
249
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27 Jul 2022
Equivariant and Invariant Grounding for Video Question Answering
ACM Multimedia (ACM MM), 2022
Yicong Li
Xiang Wang
Junbin Xiao
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213
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Calibrate to Interpret
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N. Posocco
Antoine Bonnefoy
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110
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Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions
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Zulqarnain Khan
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A. Masoomi
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380
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Robustness of Explanation Methods for NLP Models
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156
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Isha Puri
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Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models
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Hadi Elzayn
Alexandra Chouldechova
Jacob Goldin
Mark A. Lemley
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Efficiently Training Low-Curvature Neural Networks
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Suraj Srinivas
Kyle Matoba
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François Fleuret
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214
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On the explainable properties of 1-Lipschitz Neural Networks: An Optimal Transport Perspective
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M. Serrurier
Franck Mamalet
Thomas Fel
Louis Bethune
Thibaut Boissin
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359
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14 Jun 2022
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure
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Paul Novello
Thomas Fel
David Vigouroux
FAtt
336
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Xplique: A Deep Learning Explainability Toolbox
Thomas Fel
Lucas Hervier
David Vigouroux
Antonin Poché
Justin Plakoo
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C. Nicodeme
Laurent Gardes
G. Flandin
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Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark
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Eyke Hüllermeier
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Ralf Krestel
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169
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Fooling Explanations in Text Classifiers
International Conference on Learning Representations (ICLR), 2022
Adam Ivankay
Ivan Girardi
Chiara Marchiori
P. Frossard
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200
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Saliency Cards: A Framework to Characterize and Compare Saliency Methods
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Harini Suresh
Hendrik Strobelt
John Guttag
Arvindmani Satyanarayan
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207
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A Human-Centric Take on Model Monitoring
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K. Kenthapadi
207
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Use-Case-Grounded Simulations for Explanation Evaluation
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Valerie Chen
Nari Johnson
Nicholay Topin
Gregory Plumb
Ameet Talwalkar
FAtt
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206
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Interpretable Mixture of Experts
Aya Abdelsalam Ismail
Sercan O. Arik
Chang Jo Kim
Ankur Taly
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Tomas Pfister
MoE
182
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05 Jun 2022
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations
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Tessa Han
Suraj Srinivas
Himabindu Lakkaraju
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349
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Attribution-based Explanations that Provide Recourse Cannot be Robust
Journal of machine learning research (JMLR), 2022
H. Fokkema
R. D. Heide
T. Erven
FAtt
362
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Scalable Interpretability via Polynomials
Neural Information Processing Systems (NeurIPS), 2022
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Filip Radenovic
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172
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Towards a Theory of Faithfulness: Faithful Explanations of Differentiable Classifiers over Continuous Data
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181
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The Solvability of Interpretability Evaluation Metrics
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J. Shah
251
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Sparse Visual Counterfactual Explanations in Image Space
German Conference on Pattern Recognition (GCPR), 2022
Valentyn Boreiko
Maximilian Augustin
Francesco Croce
Philipp Berens
Matthias Hein
BDL
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351
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16 May 2022
Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection
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Fan Wang
A. Kong
313
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15 May 2022
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2022
Jessica Dai
Sohini Upadhyay
Ulrich Aïvodji
Stephen H. Bach
Himabindu Lakkaraju
249
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Explainable Deep Learning Methods in Medical Image Classification: A Survey
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263
101
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Should attention be all we need? The epistemic and ethical implications of unification in machine learning
Conference on Fairness, Accountability and Transparency (FAccT), 2022
N. Fishman
Leif Hancox-Li
180
11
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09 May 2022
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