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For an explanation of a deep learning model to be effective, it must provide both insight into a model and suggest a corresponding action in order to achieve some objective.
The mnist database of handwritten digits
LeCun, Y · 1998
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Using “annotator rationales” to improve machine learning for text categorization
Zaidan, O., Eisner, J., and Piatko, C · 2007
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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How to explain individual classification decisions
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., and Müller, K.-R · 2010
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Slic superpixels compared to state-of-the-art superpixel methods
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Süsstrunk, S · 2012
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. 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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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
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Man is to computer programmer as woman is to homemaker? debiasing word embeddings
Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V., and Kalai, A. T · 2016
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Compas risk scales: Demonstrating accuracy equity and predictive parity
Dieterich, W., Mendoza, C., and Brennan, T · 2016
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How we analyzed the compas recidivism algorithm
Larson, J., Mattu, S., Kirchner, L., and Angwin, 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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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 · 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
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Real time image saliency for black box classifiers
Dabkowski, P. and Gal, Y · 2017
Earlier work this paper cites.
Towards a rigorous science of interpretable machine learning
Doshi-Velez, F. and Kim, B · 2017
Earlier work this paper cites.
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., and Thrun, S · 2017
Earlier work this paper cites.
Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
Automatic rule extraction from long short term memory networks
Murdoch, W. J. and Szlam, A · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
Right for the right reasons: Training differentiable models by constraining their explanations
Ross, A. S., Hughes, M. C., and Doshi-Velez, F · 2017
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.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Tschandl, P., Rosendahl, C., and Kittler, H · 2018
Later among the works it cites.
Codella, N., Rotemberg, V., Tschandl, P., Celebi, M. E., Dusza, S., Gutman, D., Helba, B., Kalloo, A., Liopyris, K., Marchetti, M., et al · 2019
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Learning credible deep neural networks with rationale regularization
Du, M., Liu, N., Yang, F., and Hu, X · 2019
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Learning explainable models using attribution priors
Erion, G., Janizek, J. D., Sturmfels, P., Lundberg, S., and Lee, S.-I · 2019
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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.
Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
Cited alongside, same era.
Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M., Ceolini, E., Oztireli, C., and Gross, M · 2018
Cited alongside, same era.
Deriving machine attention from human rationales
Bao, Y., Chang, S., Yu, M., and Barzilay, R · 2018
Cited alongside, same era.
Women also snowboard: Overcoming bias in captioning models
Burns, K., Hendricks, L. A., Saenko, K., Darrell, T., and Rohrbach, A · 2018
Cited alongside, same era.
The accuracy, fairness, and limits of predicting recidivism
Dressel, J. and Farid, H · 2018
Cited alongside, same era.
Jain, S. and Wallace, B. C · 2019
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Repair: Removing representation bias by dataset resampling
Li, Y. and Vasconcelos, N · 2019
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Incorporating priors with feature attribution on text classification
Liu, F. and Avci, B · 2019
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Embedding human knowledge in deep neural network via attention map
Mitsuhara, M., Fukui, H., Sakashita, Y., Ogata, T., Hirakawa, T., Yamashita, T., and Fujiyoshi, H · 2019
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Interpretable machine learning: definitions, methods, and applications
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., and Yu, B · 2019
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Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S · 2019
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Pytorch cnn visualizations
Ozbulak, U · 2019
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Aggregating explainability methods for neural networks stabilizes explanations
Rieger, L. and Hansen, L. K · 2019
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Hierarchical interpretations for neural network predictions
Singh, C., Murdoch, W. J., and Yu, B · 2019
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Do human rationales improve machine explanations?
Strout, J., Zhang, Y., and Mooney, R. J · 2019
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Learning robust representations by projecting superficial statistics out
Wang, H., He, Z., Lipton, Z. C., and Xing, E. P · 2019
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Association Between Surgical Skin Markings in Dermoscopic Images and Diagnostic Performance of a Deep Learning Convolutional Neural Network for Melanoma RecognitionSurgical Skin Markings in Dermoscopic Images and Deep Learning Convolutional Neural Network Recognition of MelanomaSurgical Skin Markings in Dermoscopic Images and Deep Learning Convolutional Neural Network Recognition of Melanoma
Winkler, J. K., Fink, C., Toberer, F., Enk, A., Deinlein, T., Hofmann-Wellenhof, R., Thomas, L., Lallas, A., Blum, A., Stolz, W., and Haenssle, H. A · 2019
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Interpreting adversarially trained convolutional neural networks
Zhang, T. and Zhu, Z · 2019
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Transformation importance with applications to cosmology, 2020
Singh, C., Ha, W., Lanusse, F., Boehm, V., Liu, J., and Yu, B · 2020
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