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With a variety of local feature attribution methods being proposed in recent years, follow-up work suggested several evaluation strategies.
Probability of Error, Equivocation, and the Chernoff Bound
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Elements of Information Theory
Cover, T. M. and Thomas, J. A · 2006
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G., and Hinton, G · 2013
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Food-101–mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Gool, L. V · 2014
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A review of feature selection methods based on mutual information
Vergara, J. R. and Estévez, P. A · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Lapuschkin, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 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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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Licon: A linear weighting scheme for the contribution ofinput variables in deep artificial neural networks
Kasneci, G. and Gottron, T · 2016
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Relation between classification accuracy and mutual information in equally weighted classification task
Meyen, S · 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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Evaluating the visualization of what a deep neural network has learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., and Müller, K.-R · 2016
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Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M., Ceolini, E., Öztireli, C., and Gross, M · 2017
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Towards a rigorous science of interpretable machine learning
Doshi-Velez, F. and Kim, B · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
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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
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Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Cited alongside, same era.
Peeking inside the black-box: a survey on explainable artificial intelligence (xai)
Adadi, A. and Berrada, M · 2018
Cited alongside, same era.
Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
Cited alongside, same era.
L-shapley and c-shapley: Efficient model interpretation for structured data
Chen, J., Song, L., Wainwright, M. J., and Jordan, M. I · 2018
Cited alongside, same era.
A baseline for shapely values in mlps: from missingness to neutrality
Izzo, C., Lipani, A., Okhrati, R., and Medda, F · 2020
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Generative imputation and stochastic prediction
Kachuee, M., Karkkainen, K., Goldstein, O., Darabi, S., and Sarrafzadeh, M · 2020
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On quantitative aspects of model interpretability
Nguyen, A. P. and Martínez, M. R · 2020
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Schramowski, P., Stammer, W., Teso, S., Brugger, A., Herbert, F., Shao, X., Luigs, H.-G., Mahlein, A.-K., and Kersting, K · 2020
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Visualizing the impact of feature attribution baselines
Sturmfels, P., Lundberg, S., and Lee, S.-I · 2020
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Rise: Randomized input sampling for explanation of black-box models
Petsiuk, V., Das, A., and Saenko, K · 2018
Cited alongside, same era.
Gain: Missing data imputation using generative adversarial nets
Yoon, J., Jordon, J., and Schaar, M · 2018
Cited alongside, same era.
Testing the robustness of attribution methods for convolutional neural networks in mri-based alzheimer’s disease classification
Eitel, F., Ritter, K., Alzheimer’s Disease Neuroimaging Initiative (ADNI), et al · 2019
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks
Hooker, S., Erhan, D., Kindermans, P. J., and Kim, B · 2019
Cited alongside, same era.
Full-gradient representation for neural network visualization
Srinivas, S. and Fleuret, F · 2019
Cited alongside, same era.
On the (in) fidelity and sensitivity of explanations
Yeh, C.-K., Hsieh, C.-Y., Suggala, A., Inouye, D. I., and Ravikumar, P. K · 2019
Cited alongside, same era.
A survey on explainable artificial intelligence (xai): Toward medical xai
Tjoa, E. and Guan, C · 2020
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Sanity checks for saliency metrics
Tomsett, R., Harborne, D., Chakraborty, S., Gurram, P., and Preece, A · 2020
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Attribution in scale and space
Xu, S., Venugopalan, S., and Sundararajan, M · 2020
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Towards rigorous interpretations: a formalisation of feature attribution
Afchar, D., Guigue, V., and Hennequin, R · 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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How good is your explanation? algorithmic stability measures to assess the qualityof explanations for deep neural networks
Fel, T., Vigouroux, D., Cadène, R., and Serre, T · 2021
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On baselines for local feature attributions
Haug, J., Zürn, S., El-Jiz, P., and Kasneci, G · 2021
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Have we learned to explain?: How interpretability methods can learn to encode predictions in their interpretations
Jethani, N., Sudarshan, M., Aphinyanaphongs, Y., and Ranganath, R · 2021
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Mimic-if: Interpretability and fairness evaluation of deep learning models on mimic-iv dataset
Meng, C., Trinh, L., Xu, N., and Liu, Y · 2021
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Do input gradients highlight discriminative features?, 2021
Shah, H., Jain, P., and Netrapalli, P · 2021
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Quantus: an explainable AI toolkit for responsible evaluation of neural network explanations
Hedström, A., Weber, L., Bareeva, D., Motzkus, F., Samek, W., Lapuschkin, S., and Höhne, M. M.-C · 2022
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Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M., and Seifert, C · 2022
Closest in time.