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Concept-based explanation approach is a popular model interpertability tool because it expresses the reasons for a model's predictions in terms of concepts that are meaningful for the domain experts.
Measurement error in nonlinear models: a modern perspective
Raymond J Carroll, David Ruppert, Leonard A Stefanski, and Ciprian M Crainiceanu · 2006
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Causality
Judea Pearl · 2009
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Instrumental variables in statistics and econometrics
James H Stock · 2015
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep IV: A flexible approach for counterfactual prediction
Jason Hartford, Greg Lewis, Kevin Leyton-Brown, and Matt Taddy · 2017
Cited alongside, same era.
Regression concept vectors for bidirectional explanations in histopathology
Mara Graziani, Vincent Andrearczyk, and Henning Müller · 2018
Cited alongside, same era.
Interactive Naming for Explaining Deep Neural Networks: A Formative Study
Mandana Hamidi-Haines, Zhongang Qi, Alan Fern, Fuxin Li, and Prasad Tadepalli · 2018
Cited alongside, same era.
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
Cited alongside, same era.
Concept Saliency Maps to Visualize Relevant Features in Deep Generative Models
Lennart Brocki and Neo Christopher Chung · 2019
Cited alongside, same era.
Towards automatic concept-based explanations
Amirata Ghorbani, James Wexler, James Y Zou, and Been Kim · 2019
Later among the works it cites.
Explaining Classifiers with Causal Concept Effect (CaCE)
Yash Goyal, Uri Shalit, and Been Kim · 2019
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A benchmark for interpretability methods in deep neural networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, and Been Kim · 2019
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Interpretability beyond classification output: Semantic bottleneck networks
Max Losch, Mario Fritz, and Bernt Schiele · 2019
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Interpretable AI for Deep Learning- Based Meteorological Applications
Conner Sprague, Eric B Wendoloski, and Ingrid Guch · 2019
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Human-centered tools for coping with imperfect algorithms during medical decision-making
Carrie J Cai, Emily Reif, Narayan Hegde, Jason Hipp, Been Kim, Daniel Smilkov, Martin Wattenberg, Fernanda Viegas, Greg S Corrado, Martin C Stumpe, et al · 2019
Cited alongside, same era.
Global and local interpretability for cardiac MRI classification
James R Clough, Ilkay Oksuz, Esther Puyol-Antón, Bram Ruijsink, Andrew P King, and Julia A Schnabel · 2019
Cited alongside, same era.
Concept Bottleneck Models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
Closest in time.
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh, Been Kim, Sercan O Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar · 2020
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