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Providing explanations along with predictions is crucial in some text processing tasks.
XOC: explainable observer-classifier for explainable binary decisions
Stephan Alaniz and Zeynep Akata · 1902
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Wordnet: A lexical database for english
George A. Miller · 1995
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Introduction to Reinforcement Learning
Richard S. Sutton and Andrew G. Barto · 1998
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Latent dirichlet allocation
David M. Blei, Andrew Y. Ng, Michael I. Jordan, and John Lafferty · 2003
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Online learning for matrix factorization and sparse coding
Julien Mairal, Francis Bach, Jean Ponce, and Guillermo Sapiro · 2010
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Learning attitudes and attributes from multi-aspect reviews
Julian McAuley, Jure Leskovec, and Dan Jurafsky · 2012
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Kaggle dogs vs cats dataset, 2013
Kaggle · 2013
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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 Ng, and Christopher Potts · 2013
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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Very deep convolutional networks for large-scale image recognition
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Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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DBpedia - a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N. Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick van Kleef, Sören Auer, and Christian Bizer · 2015
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Xiang Zhang and Yann LeCun · 2015
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Latent lstm allocation: Joint clustering and non-linear dynamic modeling of sequence data
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Towards robust interpretability with self-explaining neural networks
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Learning word vectors for 157 languages
Edouard Grave, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, and Rory Sayres · 2018
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Interpretable classification via supervised variational autoencoders and differentiable decision trees, 2018
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Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
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"Why Should I Trust You?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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A causal framework for explaining the predictions of black-box sequence-to-sequence models
David Alvarez-Melis and Tommi S. Jaakkola · 2017
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Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville
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Eleanor Quint, Garrett Wirka, Jacob Williams, Stephen Scott, and N.V. Vinodchandran · 2018
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Interpretable neural predictions with differentiable binary variables, 2019
Joost Bastings, Wilker Aziz, and Ivan Titov · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Counterfactual visual explanations, 2019
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Learning corresponded rationales for text matching, 2019
Mo Yu, Shiyu Chang, and Tommi S Jaakkola · 2019
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