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Recently, a series of studies have tried to extract interactions between input variables modeled by a DNN and define such interactions as concepts encoded by the DNN.
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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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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The mnist database of handwritten digits
LeCun, Y · 1998
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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An axiomatic approach to the concept of interaction among players in cooperative games
Grabisch, M. and Roubens, M · 1999
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Detecting statistical interactions with additive groves of trees
Sorokina, D., Caruana, R., Riedewald, M., and Fink, D · 2008
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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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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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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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. Y., and Potts, C · 2013
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Convolutional neural networks for sentence classification
Kim, Y · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Understanding neural networks through deep visualization
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., and Lipson, H · 2015
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Object detectors emerge in deep scene cnns
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2015
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Interpretable deep models for icu outcome prediction
Che, Z., Purushotham, S., Khemani, R., and Liu, Y · 2016
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Inverting visual representations with convolutional networks
Dosovitskiy, A. and Brox, T · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
" why should i trust you?" explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Cited alongside, same era.
A scalable active framework for region annotation in 3d shape collections
Yi, L., Kim, V. G., Ceylan, D., Shen, I.-C., Yan, M., Su, H., Lu, C., Huang, Q., Sheffer, A., and Guibas, L · 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.
Network dissection: Quantifying interpretability of deep visual representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A · 2017
Learning global additive explanations for neural nets using model distillation
Tan, S., Caruana, R., Hooker, G., Koch, P., and Gordo, A · 2018
Later among the works it cites.
Explainable neural networks based on additive index models
Vaughan, J., Sudjianto, A., Brahimi, E., Chen, J., and Nair, V. N · 2018
Later among the works it cites.
Beyond sparsity: Tree regularization of deep models for interpretability
Wu, M., Hughes, M. C., Parbhoo, S., Zazzi, M., Roth, V., and Doshi-Velez, F · 2018
Later among the works it cites.
Interpreting cnn knowledge via an explanatory graph
Zhang, Q., Cao, R., Shi, F., Wu, Y. N., and Zhu, S.-C · 2018
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Explaining deep neural networks with a polynomial time algorithm for shapley value approximation
Ancona, M., Oztireli, C., and Gross, M · 2019
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Cited alongside, same era.
Real time image saliency for black box classifiers
Dabkowski, P. and Gal, Y · 2017
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.
Distilling a neural network into a soft decision tree
Frosst, N. and Hinton, G · 2017
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
Towards hierarchical importance attribution: Explaining compositional semantics for neural sequence models
Jin, X., Wei, Z., Du, J., Xue, X., and Ren, X · 2019
Later among the works it cites.
Neural network acceptability judgments
Warstadt, A., Singh, A., and Bowman, S. R · 2019
Later among the works it cites.
Understanding global feature contributions with additive importance measures
Covert, I., Lundberg, S. M., and Lee, S.-I · 2020
Later among the works it cites.
Explaining explanations: Axiomatic feature interactions for deep networks
Janizek, J. D., Sturmfels, P., and Lee, S.-I · 2020
Later among the works it cites.
The shapley taylor interaction index
Sundararajan, M., Dhamdhere, K., and Agarwal, A · 2020
Later among the works it cites.
Interpreting representation quality of DNNs for 3d point cloud processing
Shen, W., Ren, Q., Liu, D., and Zhang, Q · 2021
Later among the works it cites.
Interpreting attributions and interactions of adversarial attacks
Wang, X., Lin, S., Zhang, H., Zhu, Y., and Zhang, Q · 2021
Later among the works it cites.
A unified approach to interpreting and boosting adversarial transferability
Wang, X., Ren, J., Lin, S., Zhu, X., Wang, Y., and Zhang, Q · 2021
Later among the works it cites.
Building interpretable interaction trees for deep NLP models
Zhang, D., Zhang, H., Zhou, H., Bao, X., Huo, D., Chen, R., Cheng, X., Wu, M., and Zhang, Q · 2021
Later among the works it cites.
Interpreting multivariate shapley interactions in DNNs
Zhang, H., Xie, Y., Zheng, L., Zhang, D., and Zhang, Q · 2021
Later among the works it cites.
Faith-shap: The faithful shapley interaction index
Tsai, C.-P., Yeh, C.-K., and Ravikumar, P · 2022
Later among the works it cites.
Defining and quantifying and-or interactions for faithful and concise explanation of DNNs
Li, M. and Zhang, Q · 2023
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Concept-level explanation for the generalization of a DNN
Zhou, H., Zhang, H., Deng, H., Liu, D., Shen, W., Chan, S.-H., and Zhang, Q · 2023
Closest in time.