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Concept-based interpretations of black-box models are often more intuitive for humans to understand.
Explaining classifiers with causal concept effect (cace)
Yash Goyal, Amir Feder, Uri Shalit, and Been Kim · 1907
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Why patients with congestive heart failure die: arrhythmias and sudden cardiac death
J. T. Bigger · 1987
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Impact of atrial fibrillation on the risk of death: the Framingham Heart Study
E. J. Benjamin, P. A. Wolf, R. B. D’Agostino, H. Silbershatz, W. B. Kannel, and D. Levy · 1998
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Long-term prognosis of acute pulmonary oedema–an ominous outcome
A. Roguin, D. Behar, H. Ben Ami, S. A. Reisner, S. Edelstein, S. Linn, and Y. Edoute · 2000
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Recurrent infarction causes the most deaths following myocardial infarction with left ventricular dysfunction
S. Orn, J. G. Cleland, M. Romo, J. Kjekshus, and K. Dickstein · 2005
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Ventricular tachycardia and sudden cardiac death
B. A. Koplan and W. G. Stevenson · 2009
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Supraventricular tachycardia
C. Medi, J. M. Kalman, and S. B. Freedman · 2009
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Fatal myocardial rupture after acute myocardial infarction complicated by heart failure, left ventricular dysfunction, or both: the VALsartan In Acute myocardial iNfarcTion Trial (VALIANT)
F. Shamshad, S. Kenchaiah, P. V. Finn, J. Soler-Soler, J. J. McMurray, E. J. Velazquez, A. P. Maggioni, R. M. Califf, K. Swedberg, L. Kober, Y. Belenkov, S. Varshavsky, M. A. Pfeffer, and S. D. Solomon · 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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Prototype selection for interpretable classification
Jacob Bien and Robert Tibshirani · 2011
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Incidence of sudden cardiac death after ventricular fibrillation complicating acute myocardial infarction: a 5-year cause-of-death analysis of the FAST-MI 2005 registry
W. Bougouin, E. Marijon, E. Puymirat, P. Defaye, D. S. Celermajer, J. Y. Le Heuzey, S. Boveda, S. Kacet, P. Mabo, C. Barnay, A. Da Costa, J. C. Deharo, J. C. Daubert, J. Ferrières, T. Simon, and N. Danchin · 2014
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Falling rule lists
Fulton Wang and Cynthia Rudin · 2015
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Complete Heart Block Complicating ST-Segment Elevation Myocardial Infarction: Temporal Trends and Association With In-Hospital Outcomes
P. Harikrishnan, T. Gupta, C. Palaniswamy, D. Kolte, S. Khera, M. Mujib, W. S. Aronow, C. Ahn, S. Sule, D. Jain, A. Ahmed, H. A. Cooper, J. Jacobson, S. Iwai, W. H. Frishman, D. L. Bhatt, G. C. Fonarow, and J. A. Panza · 2015
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Angina and Future Cardiovascular Events in Stable Patients With Coronary Artery Disease: Insights From the Reduction of Atherothrombosis for Continued Health (REACH) Registry
A. Eisen, D. L. Bhatt, P. G. Steg, K. A. Eagle, S. Goto, J. Guo, S. C. Smith, E. M. Ohman, and B. M. Scirica · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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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 · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Dressler syndrome
Ari D Leib, Lisa A Foris, Tran Nguyen, and Karam Khaddour · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling · 2017
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Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
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Educe: Explaining model decisions through unsupervised concepts extraction
Diane Bouchacourt and Ludovic Denoyer · 2019
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This looks like that: deep learning for interpretable image recognition
Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su · 2019
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Functional transparency for structured data: a game-theoretic approach
Guang-He Lee, Wengong Jin, David Alvarez-Melis, and Tommi Jaakkola · 2019
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S. Joshi, O. Koyejo, Warut D. Vijitbenjaronk, Been Kim, and Joydeep Ghosh · 2019
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On the (in)fidelity and sensitivity of explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala, David I. Inouye, and Pradeep Ravikumar · 2019
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Pang Wei Koh and Percy Liang · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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.
Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly
Yongqin Xian, Christoph H Lampert, Bernt Schiele, and Zeynep Akata · 2018
Cited alongside, same era.
Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Cited alongside, same era.
Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Amit Dhurandhar, Pin-Yu Chen, Ronny Luss, Chun-Chen Tu, Paishun Ting, Karthikeyan Shanmugam, and Payel Das · 2018
Cited alongside, same era.
Grounding visual explanations
Lisa Anne Hendricks, Ronghang Hu, Trevor Darrell, and Zeynep Akata · 2018
Cited alongside, same era.
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Certified adversarial robustness via randomized smoothing
Jeremy M Cohen, Elan Rosenfeld, and J Zico Kolter · 2019
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Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
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Face: Feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodríguez, T. D. Bie, and Peter A. Flach · 2020
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Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan · 2020
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On completeness-aware concept-based explanations in deep neural networks
Chih-Kuan Yeh, Been Kim, Sercan Arik, Chun-Liang Li, Tomas Pfister, and Pradeep Ravikumar · 2020
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Debiasing concept-based explanations with causal analysis
Mohammad Taha Bahadori and David Heckerman · 2020
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Concept whitening for interpretable image recognition
Zhi Chen, Yijie Bei, and Cynthia Rudin · 2020
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Best of both worlds: local and global explanations with human-understandable concepts
Jessica Schrouff, Sebastien Baur, Shaobo Hou, Diana Mincu, Eric Loreaux, Ralph Blanes, James Wexler, Alan Karthikesalingam, and Been Kim · 2021
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Evaluations and methods for explanation through robustness analysis
Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu, Pradeep Kumar Ravikumar, Seungyeon Kim, Sanjiv Kumar, and Cho-Jui Hsieh · 2021
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Dissect: Disentangled simultaneous explanations via concept traversals
Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, and Rosalind W Picard · 2021
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Concept activation regions: A generalized framework for concept-based explanations
Jonathan Crabbé and Mihaela van der Schaar · 2022
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