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Post-hoc explanation methods are an important class of approaches that help understand the rationale underlying a trained model's decision.
The argument reasoning comprehension task: Identification and reconstruction of implicit warrants
Ivan Habernal, Henning Wachsmuth, Iryna Gurevych, and Benno Stein. 2018 · 1940
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges. 2010 · 2010
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
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"why should i trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
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Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez. 2017 · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Unmasking clever hans predictors and assessing what machines really learn
Sebastian Lapuschkin, Stephan Wäldchen, Alexander Binder, Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. 2019 · 2019
Cited alongside, same era.
Incorporating priors with feature attribution on text classification
Frederick Liu and Besim Avci. 2019 · 2019
Cited alongside, same era.
Probing neural network comprehension of natural language arguments
Timothy Niven and Hung-Yu Kao. 2019 · 2019
Cited alongside, same era.
Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020 · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann. 2020 · 2020
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Captum: A unified and generic model interpretability library for pytorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, and Orion Reblitz-Richardson. 2020 · 2020
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FIND: Human-in-the-Loop Debugging Deep Text Classifiers
Piyawat Lertvittayakumjorn, Lucia Specia, and Francesca Toni. 2020 · 2020
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Interpretations are useful: Penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, W. James Murdoch, and Bin Yu. 2020 · 2020
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Julius Adebayo, Michael Muelly, Ilaria Liccardi, and Been Kim. 2020 · 2020
Cited alongside, same era.
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