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Recently, several methods have been proposed to explain the predictions of recurrent neural networks (RNNs), in particular of LSTMs.
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
Afra Alishahi, Grzegorz Chrupala, and Tal Linzen. 2019 · 1904
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Use of some sensitivity criteria for choosing networks with good generalization ability
Yannis Dimopoulos, Paul Bourret, and Sovan Lek. 1995 · 1995
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LSTM can Solve Hard Long Time Lag Problems
Sepp Hochreiter and Jürgen Schmidhuber. 1996 · 1996
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Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Bidirectional Recurrent Neural Networks
Mike Schuster and Kuldip K. Paliwal. 1997 · 1997
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Learning to Forget: Continual Prediction with LSTM
Felix A. Gers, Jürgen Schmidhuber, and Fred Cummins. 1999 · 1999
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Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler and Rob Fergus. 2014 · 1999
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Review and comparison of methods to study the contribution of variables in artificial neural network models
Muriel Gevrey, Ioannis Dimopoulos, and Sovan Lek. 2003 · 2003
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The meaning of negated adjectives
Tamar Fraenkel and Yaacov Schul. 2008 · 2008
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How to Explain Individual Classification Decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller. 2010 · 2010
Earlier work this paper cites.
Learning Recurrent Neural Networks with Hessian-Free Optimization
James Martens and Ilya Sutskever. 2011 · 2011
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Interpreting Individual Classifications of Hierarchical Networks
Will Landecker, Michael D. Thomure, Luís M. A. Bettencourt, Melanie Mitchell, Garrett T. Kenyon, and Steven P. Brumby. 2013 · 2013
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Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning, Andrew Y. Ng, and Christopher Potts. 2013 · 2013
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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014 · 2014
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On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015 · 2015
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Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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Extraction of Salient Sentences from Labelled Documents
Misha Denil, Alban Demiraj, and Nando de Freitas. 2015 · 2015
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Deep visual-semantic alignments for generating image descriptions
Andrej Karpathy and Li Fei-Fei. 2015 · 2015
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A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
Quoc V. Le, Navdeep Jaitly, and Geoffrey E. Hinton. 2015 · 2015
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Striving for Simplicity: The All Convolutional Net
Jost T. Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. 2015 · 2015
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Explaining Predictions of Non-Linear Classifiers in NLP
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2016 · 2016
Cited alongside, same era.
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016 · 2016
Cited alongside, same era.
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)
EU-GDPR. 2016 · 2016
Cited alongside, same era.
Investigating the influence of noise and distractors on the interpretation of neural networks
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller, and Sven Dähne. 2016 · 2016
Cited alongside, same era.
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
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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Sanity Checks for Saliency Maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018 · 2018
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross. 2018 · 2018
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RUDDER: Return Decomposition for Delayed Rewards
Jose A. Arjona-Medina, Michael Gillhofer, Michael Widrich, Thomas Unterthiner, Johannes Brandstetter, and Sepp Hochreiter. 2018 · 2018
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Sebastian Lapuschkin, Alexander Binder, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2016 · 2016
Cited alongside, same era.
Visualizing and Understanding Neural Models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016 · 2016
Cited alongside, same era.
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
Cited alongside, same era.
”Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
Cited alongside, same era.
Reasoning about Entailment with Neural Attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomas Kocisky, and Phil Blunsom. 2016 · 2016
Cited alongside, same era.
Not Just A Black Box: Interpretable Deep Learning by Propagating Activation Differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje. 2016 · 2016
Cited alongside, same era.
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
Huijuan Xu and Kate Saenko. 2016 · 2016
Cited alongside, same era.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan. 2018 · 2018
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Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
Fréderic Godin, Kris Demuynck, Joni Dambre, Wesley De Neve, and Thomas Demeester. 2018 · 2018
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Understanding Convolutional Neural Networks for Text Classification
Alon Jacovi, Oren Sar Shalom, and Yoav Goldberg. 2018 · 2018
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Methods for interpreting and understanding deep neural network
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. 2018 · 2018
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On the importance of single directions for generalization
Ari S. Morcos, David G.T. Barrett, Neil C. Rabinowitz, and Matthew Botvinick. 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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Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement
Nina Poerner, Hinrich Schütze, and Benjamin Roth. 2018 · 2018
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An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs
Florian Strohm and Roman Klinger. 2018 · 2018
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Explaining Therapy Predictions with Layer-Wise Relevance Propagation in Neural Networks
Yinchong Yang, Volker Tresp, Marius Wunderle, and Peter A. Fasching. 2018 · 2018
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Analysis Methods in Neural Language Processing: A Survey
Yonatan Belinkov and James Glass. 2019 · 2019
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The Emergence of Number and Syntax Units in LSTM Language Models
Yair Lakretz, Germán Kruszewski, Théo Desbordes, Dieuwke Hupkes, Stanislas Dehaene, and Marco Baroni. 2019 · 2019
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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
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DRAU: Dual Recurrent Attention Units for Visual Question Answering
Ahmed Osman and Wojciech Samek. 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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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, and Yoshua Bengio. 2015 · 2057
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