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The Shapley value is widely regarded as a trustworthy attribution metric.
A value for n-person games
Shapley, L. S · 1953
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A simplified bargaining model for the n-person cooperative game
Harsanyi, J. C · 1963
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Monotonic solutions of cooperative games
Young, H. P · 1985
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Probabilistic values for games
Weber, R. J · 1988
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Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
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Polynomial calculation of the shapley value based on sampling
Castro, J., Gómez, D., and Tejada, J · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Mnist handwritten digit database, 2010
LeCun, Y. and Cortes, C · 2010
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An efficient explanation of individual classifications using game theory
Strumbelj, E. and Kononenko, I · 2010
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A · 2013
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2015
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Set functions, games and capacities in decision making , volume 46
Grabisch, M. et al · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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”why should i trust you?” explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Not just a black box: Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., Shcherbina, A., and Kundaje, A · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2016
Cited alongside, same era.
UCI machine learning repository, 2017
Dua, D. and Graff, C · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., and Müller, K.-R · 2017
The many shapley values for model explanation
Sundararajan, M. and Najmi, A · 2020
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Covid-net: a tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray image
Wang, L., Lin, Z. Q., and Wong, A · 2020
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Improving kernelshap: Practical shapley value estimation using linear regression
Covert, I. and Lee, S.-I · 2021
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Explaining by removing: A unified framework for model explanation
Covert, I., Lundberg, S., and Lee, S.-I · 2021
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Mutual information preserving back-propagation: Learn to invert for faithful attribution
Deng, H., Zou, N., Chen, W., Feng, G., Du, M., and Hu, X · 2021
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Fastshap: Real-time shapley value estimation
Jethani, N., Sudarshan, M., Covert, I. C., Lee, S.-I., and Ranganath, R · 2021
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Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Cited alongside, same era.
Visualizing deep neural network decisions: Prediction difference analysis
Zintgraf, L. M., Cohen, T. S., Adel, T., and Welling, M · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Cited alongside, same era.
Model agnostic supervised local explanations
Plumb, G., Molitor, D., and Talwalkar, A. S · 2018
Cited alongside, same era.
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Scalability vs. utility: Do we have to sacrifice one for the other in data importance quantification?
Jia, R., Wu, F., Sun, X., Xu, J., Dao, D., Kailkhura, B., Zhang, C., Li, B., and Song, D · 2021
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A multilinear sampling algorithm to estimate shapley values
Okhrati, R. and Lipani, A · 2021
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Shapley explanation networks
Wang, R., Wang, X., and Inouye, D · 2021
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Resnet strikes back: An improved training procedure in timm
Wightman, R., Touvron, H., and Jégou, H · 2021
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Algorithms to estimate shapley value feature attributions
Chen, H., C. Covert, I., M. Lundberg, S., and Lee, S.-I · 2022
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Discovering and explaining the representation bottleneck of dnns
Deng, H., Ren, Q., Zhang, H., and Zhang, Q · 2022
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Sampling permutations for shapley value estimation
Mitchell, R., Cooper, J., Frank, E., and Holmes, G · 2022
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Covid-net cxr-2: An enhanced deep convolutional neural network design for detection of covid-19 cases from chest x-ray images
Pavlova, M., Terhljan, N., G Chung, A., Zhao, A., Surana, S., Aboutalebi, H., Gunraj, H., Sabri, A., Alaref, A., and Wong, A · 2022
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Resmlp: Feedforward networks for image classification with data-efficient training
Touvron, H., Bojanowski, P., Caron, M., Cord, M., El-Nouby, A., Grave, E., Izacard, G., Joulin, A., Synnaeve, G., Verbeek, J., et al · 2022
Later among the works it cites.
Accelerating shapley explanation via contributive cooperator selection
Wang, G., Chuang, Y.-N., Du, M., Yang, F., Zhou, Q., Tripathi, P., Cai, X., and Hu, X · 2022
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Does a neural network really encode symbolic concepts?
Li, M. and Zhang, Q · 2023
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