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We investigate whether post-hoc model explanations are effective for diagnosing model errors--model debugging.
A value for n-person games
Lloyd S Shapley · 1988
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Generating interactive explanations
Alison Cawsey · 1991
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User modelling in interactive explanations
Alison Cawsey · 1993
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Generating patient-specific interactive natural language explanations
Giuseppe Carenini, Vibhu O Mittal, and Johanna D Moore · 1994
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Explaining collaborative filtering recommendations
Jonathan L Herlocker, Joseph A Konstan, and John Riedl · 2000
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert MÞller · 2010
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Novel dataset for fine-grained image categorization: Stanford dogs
Aditya Khosla, Nityananda Jayadevaprakash, Bangpeng Yao, and Fei-Fei Li · 2011
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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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Rules of Machine Learning: Best Practices for ML Engineering
Zinkevich Martin · 2011
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Hidden technical debt in machine learning systems
David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2016
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Debugging machine learning models
Gabriel Cadamuro, Ran Gilad-Bachrach, and Xiaojin Zhu · 2016
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Debugging machine learning tasks
Aleksandar Chakarov, Aditya Nori, Sriram Rajamani, Shayak Sen, and Deepak Vijaykeerthy · 2016
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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
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Salient deconvolutional networks
Aravindh Mahendran and Andrea Vedaldi · 2016
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Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Bach, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 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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Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2016
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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A unified approach to interpreting model predictions
Learning explainable models using attribution priors
Gabriel Erion, Joseph D Janizek, Pascal Sturmfels, Scott Lundberg, and Su-In Lee · 2019
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What can ai do for me? evaluating machine learning interpretations in cooperative play
Shi Feng and Jordan Boyd-Graber · 2019
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Interpretation of neural networks is fragile
Amirata Ghorbani, Abubakar Abid, and James Y. Zou · 2019
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Fooling neural network interpretations via adversarial model manipulation
Juyeon Heo, Sunghwan Joo, and Taesup Moon · 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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Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Right for the right reasons: Training differentiable models by constraining their explanations
Andrew Slavin Ross, Michael C. Hughes, and Finale Doshi-Velez · 2017
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Comparative study of computational visual attention models on two-dimensional medical images
Gezheng Wen, Brenda Rodriguez-Niño, Furkan Y Pecen, David J Vining, Naveen Garg, and Mia K Markey · 2017
Cited alongside, same era.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
Cited alongside, same era.
Vivian Lai and Chenhao Tan · 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
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Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy
Rory Sayres, Ankur Taly, Ehsan Rahimy, Katy Blumer, David Coz, Naama Hammel, Jonathan Krause, Arunachalam Narayanaswamy, Zahra Rastegar, Derek Wu, et al · 2019
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When explanations lie: Why modified bp attribution fails
Leon Sixt, Maximilian Granz, and Tim Landgraf · 2019
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On the (in) fidelity and sensitivity of explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Suggala, David I Inouye, and Pradeep K Ravikumar · 2019
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Evaluating saliency map explanations for convolutional neural networks: a user study
Ahmed Alqaraawi, Martin Schuessler, Philipp Weiß, Enrico Costanza, and Nadia Berthouze · 2020
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Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley · 2020
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Are visual explanations useful? a case study in model-in-the-loop prediction
Eric Chu, Deb Roy, and Jacob Andreas · 2020
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Towards best practice in explaining neural network decisions with lrp
Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, and Sebastian Lapuschkin · 2020
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" how do i fool you?" manipulating user trust via misleading black box explanations
Himabindu Lakkaraju and Osbert Bastani · 2020
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International evaluation of an ai system for breast cancer screening
Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg C Corrado, Ara Darzi, et al · 2020
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Estimating training data influence by tracking gradient descent
Garima Pruthi, Frederick Liu, Mukund Sundararajan, and Satyen Kale · 2020
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Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, W James Murdoch, and Bin Yu · 2020
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How useful are the machine-generated interpretations to general users? a human evaluation on guessing the incorrectly predicted labels
Hua Shen and Ting-Hao Huang · 2020
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Fooling lime and shap: Adversarial attacks on post hoc explanation methods
Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, and Himabindu Lakkaraju · 2020
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Sanity checks for saliency metrics
Richard Tomsett, Dan Harborne, Supriyo Chakraborty, Prudhvi Gurram, and Alun D. Preece · 2020
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