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Deep neural networks are being used increasingly to automate data analysis and decision making, yet their decision-making process is largely unclear and is difficult to explain to the end users.
Producing Explanations and Justifications of Expert Consulting Programs
William R Swartout · 1981
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Explanation in Second Generation Expert Systems
William R Swartout and Johanna D Moore · 1993
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Extracting Rules from Artificial Neural Networks with Distributed Representations
Sebastian Thrun · 1995
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Extracting Comprehensible Models from Trained Neural Networks
Mark W Craven and Jude W Shavlik · 1996
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The Role of Trust in Automation Reliance
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The Structure and Function of Explanations
Tania Lombrozo · 2006
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The Pascal Visual Object Classes (VOC) Challenge
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The Instrumental Value of Explanations
Tania Lombrozo · 2011
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Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Microsoft coco: Common objects in context
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Understanding Neural Networks Through Deep Visualization
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Visualizing and Understanding Convolutional Networks
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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, Alexander C. Berg, and Li Fei-Fei · 2015
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan and Andrew Zisserman · 2015
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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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Learning Deep Features for Discriminative Localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2016
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Real Time Image Saliency for Black Box Classifiers
Piotr Dabkowski and Yarin Gal · 2017
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Interpretable Explanations of Black Boxes by Meaningful Perturbation
Ruth C Fong and Andrea Vedaldi · 2017
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The Promise and Peril of Human Evaluation for Model Interpretability
Bernease Herman · 2017
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Learning to Reason: End-To-End Module Networks for Visual Question Answering
Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Kate Saenko · 2017
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
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Learning to Compose Neural Networks for Question Answering
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Deep Residual Learning for Image Recognition
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The Mythos of Model Interpretability
Zachary C Lipton · 2016
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Top-Down Visual Saliency Guided by Captions
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Grad-CAM: Visual Explanations From Deep Networks via Gradient-Based Localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Top-down Neural Attention by Excitation Backprop
Jianming Zhang, Sarah Adel Bargal, Zhe Lin, Xiaohui Shen Jonathan Brandt, and Stan Sclaroff · 2017
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Multimodal Explanations: Justifying Decisions and Pointing to the Evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach · 2018
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Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer W Vaughan, and Hanna Wallach · 2018
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