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Algorithmic approaches to interpreting machine learning models have proliferated in recent years.
Explaining a black-box using Deep Variational Information Bottleneck Approach
Seojin Bang, Pengtao Xie, Heewook Lee, Wei Wu, and Eric Xing. 2019 · 1902
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An introduction to the bootstrap
Bradley Efron and Robert J Tibshirani. 1994 · 1994
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Thumbs up?: sentiment classification using machine learning techniques
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002 · 2002
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Crowdsourcing Evaluations of Classifier Interpretability
Amanda Hutton, Alexander Liu, and Cheryl Martin. 2012 · 2012
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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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Visualizing and Understanding Neural Models in NLP
Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. 2016 · 2016
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The Mythos of Model Interpretability
Zachary C. Lipton. 2016 · 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 · 2016
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Hierarchical Attention Networks for Document Classification
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016 · 2016
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Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
Cited alongside, same era.
UCI machine learning repository
Dheeru Dua and Casey Graff. 2017 · 2017
Cited alongside, same era.
A Unified Approach to Interpreting Model Predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
Cited alongside, same era.
Axiomatic Attribution for Deep Networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Cited alongside, same era.
Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. 2018 · 2018
Cited alongside, same era.
Comparing Automatic and Human Evaluation of Local Explanations for Text Classification
Dong Nguyen. 2018 · 2018
Later among the works it cites.
Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2018 · 2018
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ExplainGAN: Model Explanation via Decision Boundary Crossing Transformations
Pouya Samangouei, Ardavan Saeedi, Liam Nakagawa, and Nathan Silberman. 2018 · 2018
Later among the works it cites.
This Looks Like That: Deep Learning for Interpretable Image Recognition
Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin. 2019 · 2019
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Interpretable Image Recognition with Hierarchical Prototypes
Peter Hase, Chaofan Chen, Oscar Li, and Cynthia Rudin. 2019 · 2019
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Do explanations make VQA models more predictable to a human?
Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh. 2018 · 2018
Cited alongside, same era.
Explaining Explanations: An Overview of Interpretability of Machine Learning
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal. 2018 · 2018
Cited alongside, same era.
xGEMs: Generating Examplars to Explain Black-Box Models
Shalmali Joshi, Oluwasanmi Koyejo, Been Kim, and Joydeep Ghosh. 2018 · 2018
Cited alongside, same era.
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, and Rory Sayres. 2018 · 2018
Cited alongside, same era.
A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim. 2019 · 2019
Later among the works it cites.
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
Cynthia Rudin. 2019 · 2019
Later among the works it cites.
Eraser: A benchmark to evaluate rationalized nlp models
Jay DeYoung, Sarthak Jain, Nazneen Fatema Rajani, Eric Lehman, Caiming Xiong, Richard Socher, and Byron C. Wallace. 2020 · 2020
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