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The successful deployment of artificial intelligence (AI) in many domains from healthcare to hiring requires their responsible use, particularly in model explanations and privacy.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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The role of trust in automation reliance
Mary T Dzindolet, Scott A Peterson, Regina A Pomranky, Linda G Pierce, and Hall P Beck · 2003
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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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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Fixing the program my computer learned: Barriers for end users, challenges for the machine
Todd Kulesza, Weng-Keen Wong, Simone Stumpf, Stephen Perona, Rachel White, Margaret M Burnett, Ian Oberst, and Andrew J Ko · 2009
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Why and why not explanations improve the intelligibility of context-aware intelligent systems
Brian Y Lim, Anind K Dey, and Daniel Avrahami · 2009
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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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Detecting emotional stress from facial expressions for driving safety
Hua Gao, Anil Yüce, and Jean-Philippe Thiran · 2014
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A data-driven approach to cleaning large face datasets
Hong Wei Ng and Stefan Winkler · 2014
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The faces of engagement: Automatic recognition of student engagementfrom facial expressions
Jacob Whitehill, Zewelanji Serpell, Yi-Ching Lin, Aysha Foster, and Javier R Movellan · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Detecting student emotions in computer-enabled classrooms
Nigel Bosch, Sidney K D’Mello, Ryan S Baker, Jaclyn Ocumpaugh, Valerie Shute, Matthew Ventura, Lubin Wang, and Weinan Zhao · 2016
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Inverting visual representations with convolutional networks
Alexey Dosovitskiy and Thomas Brox · 2016
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A guide to convolution arithmetic for deep learning
Vincent Dumoulin and Francesco Visin · 2016
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Regulation eu 2016/679 of the european parliament and of the council of 27 april 2016
General Data Protection Regulation · 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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Learning deep features for discriminative localization
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 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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Explanation and justification in machine learning: A survey
Or Biran and Courtenay Cotton · 2017
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Activis: Visual exploration of industry-scale deep neural network models
Minsuk Kahng, Pierre Y Andrews, Aditya Kalro, and Duen Horng Chau · 2017
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Large-scale celebfaces attributes (celeba) dataset
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2018
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End-to-end trained cnn encoder-decoder networks for image steganography
Rafia Rahim, Shahroz Nadeem, et al · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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Visual interpretability for deep learning: a survey
Quan-shi Zhang and Song-Chun Zhu · 2018
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Residual dense network for image super-resolution
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu · 2018
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Model inversion attacks against collaborative inference
Zecheng He, Tianwei Zhang, and Ruby B Lee · 2019
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Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer
Nikos Komodakis and Sergey Zagoruyko · 2017
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Dominant and complementary multi-emotional facial expression recognition using c-support vector classification
Christer Loob, Pejman Rasti, Iiris Lüsi, Julio CS Jacques, Xavier Baró, Sergio Escalera, Tomasz Sapinski, Dorota Kaminska, and Gholamreza Anbarjafari · 2017
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
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Real-time driver drowsiness detection for embedded system using model compression of deep neural networks
Bhargava Reddy, Ye-Hoon Kim, Sojung Yun, Chanwon Seo, and Junik Jang · 2017
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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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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Summit: Scaling deep learning interpretability by visualizing activation and attribution summarizations
Fred Hohman, Haekyu Park, Caleb Robinson, and Duen Horng Polo Chau · 2019
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Memguard: Defending against black-box membership inference attacks via adversarial examples
Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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Model reconstruction from model explanations
Smitha Milli, Ludwig Schmidt, Anca D Dragan, and Moritz Hardt · 2019
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Privacy risks of explaining machine learning models
Reza Shokri, Martin Strobel, and Yair Zick · 2019
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Designing theory-driven user-centric explainable ai
Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim · 2019
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Neural network inversion in adversarial setting via background knowledge alignment
Ziqi Yang, Jiyi Zhang, Ee-Chien Chang, and Zhenkai Liang · 2019
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Vision-infused deep audio inpainting
Hang Zhou, Ziwei Liu, Xudong Xu, Ping Luo, and Xiaogang Wang · 2019
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Model extraction from counterfactual explanations
Ulrich Aïvodji, Alexandre Bolot, and Sébastien Gambs · 2020
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Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
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Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization
Harish Guruprasad Ramaswamy et al · 2020
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The secret revealer: generative model-inversion attacks against deep neural networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2020
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