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With the increased usage of artificial intelligence (AI), it is imperative to understand how these models work internally.
On information and sufficiency
S. Kullback and R. A. Leibler · 1951
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Multilayer feedforward networks are universal approximators
K. Hornik, M. Stinchcombe, and H. White · 1989
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A metric for distributions with applications to image databases
Y. Rubner, C. Tomasi, and L. J. Guibas · 1998
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OptiLIME: Optimized LIME explanations for diagnostic computer algorithms
G. Visani, E. Bagli, and F. Chesani · 2006
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Using sensitivity analysis and visualization techniques to open black box data mining models
P. Cortez and M. J. Embrechts · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Saliency and human fixations: State-of-the-art and study of comparison metrics
N. Riche, M. Duvinage, M. Mancas, B. Gosselin, and T. Dutoit · 2013
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" why should i trust you?" explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
Sanity checks for saliency maps
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
Cited alongside, same era.
Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
A. Barredo Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. Garcia, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, and F. Herrera · 2019
Cited alongside, same era.
Explanation in artificial intelligence: Insights from the social sciences
T. Miller · 2019
Cited alongside, same era.
Evaluating local explanation methods on ground truth
Sanity checks for saliency metrics
R. Tomsett, D. Harborne, S. Chakraborty, P. Gurram, and A. Preece · 2020
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Clevr-xai: A benchmark dataset for the ground truth evaluation of neural network explanations
L. Arras, A. Osman, and W. Samek · 2021
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Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience
A. Mamalakis, E. A. Barnes, and I. Ebert-Uphoff · 2022
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Evaluating explainable artificial intelligence for x-ray image analysis
M. Miró-Nicolau, G. Moyà-Alcover, and A. Jaume-i Capó · 2022
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Generating perturbation-based explanations with robustness to out-of-distribution data
L. Qiu, Y. Yang, C. C. Cao, Y. Zheng, H. Ngai, J. Hsiao, and L. Chen · 2022
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R. Guidotti · 2020
Cited alongside, same era.
On the robustness of interpretability methods
D. Alvarez-Melis and T. S. Jaakkola
Cited in the paper.
Definitions, methods, and applications in interpretable machine learning
W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu
Cited in the paper.
Fooling LIME and SHAP: Adversarial attacks on post hoc explanation methods
D. Slack, S. Hilgard, E. Jia, S. Singh, and H. Lakkaraju
Cited in the paper.
B. H. van der Velden, H. J. Kuijf, K. G. Gilhuijs, and M. A. Viergever · 2022
Later among the works it cites.