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Generating explanations for neural networks has become crucial for their applications in real-world with respect to reliability and trustworthiness.
Ls-tree: Model interpretation when the data are linguistic
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A value for n-person games
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Long short-term memory
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Axiomatic characterizations of probabilistic and cardinal-probabilistic interaction indices
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An efficient explanation of individual classifications using game theory
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Distributed representations of words and phrases and their compositionality
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Recursive deep models for semantic compositionality over a sentiment treebank
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko. 2014 · 2014
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Enhanced lstm for natural language inference
Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2016 · 2016
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Anupam Datta, Shayak Sen, and Yair Zick. 2016 · 2016
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Interpretation of prediction models using the input gradient
Yotam Hechtlinger. 2016 · 2016
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Natural language object retrieval
Ronghang Hu, Huazhe Xu, Marcus Rohrbach, Jiashi Feng, Kate Saenko, and Trevor Darrell. 2016 · 2016
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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Understanding neural networks through representation erasure
Jiwei Li, Will Monroe, and Dan Jurafsky. 2016 · 2016
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The mythos of model interpretability
Zachary C Lipton. 2016 · 2016
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Interpreting recurrent and attention-based neural models: a case study on natural language inference
Reza Ghaeini, Xiaoli Z Fern, and Prasad Tadepalli. 2018 · 2018
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Fréderic Godin, Kris Demuynck, Joni Dambre, Wesley De Neve, and Thomas Demeester. 2018 · 2018
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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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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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller. 2016 · 2016
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Interactive visualization and manipulation of attention-based neural machine translation
Jaesong Lee, Joong-Hwi Shin, and Jun-Seok Kim. 2017 · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017 · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
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Towards robust interpretability with self-explaining neural networks
David Alvarez-Melis and Tommi S Jaakkola. 2018 · 2018
Cited alongside, same era.
Alon Jacovi, Oren Sar Shalom, and Yoav Goldberg. 2018 · 2018
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Jaap Jumelet and Dieuwke Hupkes. 2018 · 2018
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Consistent individualized feature attribution for tree ensembles
Scott M Lundberg, Gabriel G Erion, and Su-In Lee. 2018 · 2018
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Beyond word importance: Contextual decomposition to extract interactions from lstms
W James Murdoch, Peter J Liu, and Bin Yu. 2018 · 2018
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Comparing automatic and human evaluation of local explanations for text classification
Dong Nguyen. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Model agnostic supervised local explanations
Gregory Plumb, Denali Molitor, and Ameet S Talwalkar. 2018 · 2018
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Can i trust you more? model-agnostic hierarchical explanations
Michael Tsang, Youbang Sun, Dongxu Ren, and Yan Liu. 2018 · 2018
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Xisen Jin, Junyi Du, Zhongyu Wei, Xiangyang Xue, and Xiang Ren. 2019 · 2019
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Hierarchical interpretations for neural network predictions
Chandan Singh, W. James Murdoch, and Bin Yu. 2019 · 2019
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