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Machine learning (ML) model explainability has received growing attention, especially in the area related to model risk and regulations.
Neural language models as psycholinguistic subjects: Representations of syntactic state
Futrell, R., Wilcox, E., Morita, T., Qian, P., Ballesteros, M., and Levy, R. (2019) · 1903
Earlier work this paper cites.
Shap values for explaining cnn-based text classification models
Zhao, W., Joshi, T., Nair, V. N., and Sudjianto, A. (2020) · 2008
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Supervisory guidance on model risk management: Sr letter 11-7 attachment
OCC (2011) · 2011
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https://www.kaggle.com/c/yelp-recsys-2013/data
Yelp (2013) · 2013
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) · 2015
Earlier work this paper cites.
Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Datta, A., Sen, S., and Zick, Y. (2016) · 2016
Earlier work this paper cites.
Model-agnostic interpretability of machine learning
Ribeiro, M. T., Singh, S., and Guestrin, C. (2016) · 2016
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I. (2017) · 2017
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Investigating the interpretability of hidden layers in deep text mining
Raaijmakers, S., Sappelli, M., and Kraaij, W. (2017) · 2017
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Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A. (2017) · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q. (2017) · 2017
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Locally interpretable models and effects based on supervised partitioning (lime-sup)
Hu, L., Chen, J., Nair, V. N., and Sudjianto, A. (2018) · 2018
Cited alongside, same era.
Understanding convolutional neural networks for text classification
Distribution-free predictive inference for regression
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L. (2018) · 2018
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Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., and Müller, K.-R. (2018) · 2018
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Explainable neural networks based on additive index models
Vaughan, J., Sudjianto, A., Brahimi, E., Chen, J., and Nair, V. N. (2018) · 2018
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Explainable recommendation via multi-task learning in opinionated text data
Wang, N., Wang, H., Jia, Y., and Yin, Y. (2018) · 2018
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innvestigate neural networks!
Alber, M., Lapuschkin, S., Seegerer, P., Hägele, M., Schütt, K. T., Montavon, G., Samek, W., Müller, K.-R., Dähne, S., and Kindermans, P.-J. (2019) · 2019
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Jacovi, A., Shalom, O. S., and Goldberg, Y. (2018) · 2018
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Analyzing and interpreting convolutional neural networks in nlp
Koupaee, M. and Wang, W. Y. (2018) · 2018
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“what is relevant in a text document?”: An interpretable machine learning approach
Arras, L., Horn, F., Montavon, G., Müller, K.-R., and Samek, W. (2017a)
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Explaining recurrent neural network predictions in sentiment analysis
Arras, L., Montavon, G., Müller, K.-R., and Samek, W. (2017b)
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Towards explainable nlp: A generative explanation framework for text classification
Liu, H., Yin, Q., and Wang, W. Y. (2018a)
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Liu, X., Chen, J., Nair, V., and Sudjianto, A. (2018b)
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Reis, J., Correia, A., Murai, F., Veloso, A., and Benevenuto, F. (2019) · 2019
Later among the works it cites.
Self-interpretable convolutional neural networks for text classification
Zhao, W., Singh, R., Joshi, T., Sudjianto, A., and Nair, V. N. (2021) · 2021
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