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The problem of explaining the behavior of deep neural networks has recently gained a lot of attention.
A value for n-person games
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The rectified gaussian distribution
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Np-completeness for calculating power indices of weighted majority games
Matsui, Y. and Matsui, T · 2001
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A Family of Algorithms for Approximate Bayesian Inference
Minka, T. P · 2001
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Polynomial calculation of the shapley value based on sampling
Castro, J., Gamez, D., and Tejada, J · 2008
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A linear approximation method for the shapley value
Fatima, S. S., Wooldridge, M., and Jennings, N. R · 2008
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Accurate telemonitoring of parkinson’s disease progression by noninvasive speech tests
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An efficient explanation of individual classifications using game theory
Strumbelj, E. and Kononenko, I · 2010
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Axiomatic attribution for multilinear functions
Sun, Y. and Sundararajan, M · 2011
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Uncertainty propagation through deep neural networks
Abdelaziz, A. H., Watanabe, S., Hershey, J. R., Vincent, E., and Kolossa, D · 2015
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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
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
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Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., and Müller, K.-R · 2017
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Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Zintgraf, L. M., Cohen, T. S., Adel, T., and Welling, M · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Datta, A., Sen, S., and Zick, Y · 2016
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European union regulations on algorithmic decision-making and a” right to explanation”
Goodman, B. and Flaxman, S · 2016
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The mythos of model interpretability
Lipton, Z. C · 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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Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D · 2016
Cited alongside, same era.
Not just a black box: Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., Shcherbina, A., and Kundaje, A · 2016
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Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M., Ceolini, E., Oztireli, C., and Gross, M · 2018
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Lightweight probabilistic deep networks
Gast, J. and Roth, S · 2018
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Learning how to explain neural networks: Patternnet and patternattribution
Kindermans, P.-J., Schütt, K. T., Alber, M., Müller, K.-R., Erhan, D., Kim, B., and Dähne, S · 2018
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Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Lundberg, S. M., Nair, B., Vavilala, M. S., Horibe, M., Eisses, M. J., Adams, T., Liston, D. E., Low, D. K.-W., Newman, S.-F., Kim, J., et al · 2018
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A theoretical explanation for perplexing behaviors of backpropagation-based visualizations
Nie, W., Zhang, Y., and Patel, A · 2018
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Explanation methods in deep learning: Users, values, concerns and challenges
Ras, G., van Gerven, M., and Haselager, P · 2018
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Understand deep neural networks through input uncertainties
Thiagarajan, J. J., Kim, I., Anirudh, R., and Bremer, P.-T · 2018
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Interpretation of neural networks is fragile
Ghorbani, A., Abid, A., and Zou, J · 2019
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