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Given the pressing need for assuring algorithmic transparency, Explainable AI (XAI) has emerged as one of the key areas of AI research.
The problem of m m rankings
M. G. Kendall and B. Babington Smith · 1939
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Bayesian Interpolation
David J. C. MacKay · 1992
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Bayesian theory
Jose M. Bernardo and Adrian F. M. Smith · 1994
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Sparse Bayesian learning and the relevance vector machine
Michael E Tipping · 2001
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Pattern recognition and machine learning
Christopher M Bishop · 2006
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Imprecision and prior-data conflict in generalized Bayesian inference
Gero Walter and Thomas Augustin · 2009
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On over-fitting in model selection and subsequent selection bias in performance evaluation
Gavin C Cawley and Nicola LC Talbot · 2010
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A formal approach to adaptive software: Continuous assurance of non-functional requirements
Antonio Filieri, Carlo Ghezzi, and Giordano Tamburrelli · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2012
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Convolutional neural networks at constrained time cost
Kaiming He and Jian Sun · 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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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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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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Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)
A. Adadi and M. Berrada · 2018
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On the Robustness of Interpretability Methods
David Alvarez-Melis and Tommi S. Jaakkola · 2018
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Grad-CAM++: Generalized gradient-based visual explanations for deep convolutional networks
A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian · 2018
Cited alongside, same era.
Explaining Deep Learning Models – A Bayesian Non-parametric Approach
Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, and Lin Lin · 2018
Cited alongside, same era.
ALIME: Autoencoder based approach for local interpretability
Sharath M. Shankaranarayana and Davor Runje · 2019
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Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao · 2019
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RL-LIM: Reinforcement Learning-based Locally Interpretable Modeling
Jinsung Yoon, Sercan O. Arik, and Tomas Pfister · 2019
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Probabilistic model checking of robots deployed in extreme environments
Xingyu Zhao, Valentin Robu, David Flynn, Fateme Dinmohammadi, Michael Fisher, and Matt Webster · 2019
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A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, and Xinping Yi · 2020
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Defining locality for surrogates in post-hoc interpretability
Thibault Laugel, Xavier Renard, Marie-Jeanne Lesot, Christophe Marsala, and Marcin Detyniecki · 2018
Cited alongside, same era.
Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 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.
Model Agnostic Supervised Local Explanations
Gregory Plumb, Denali Molitor, and Ameet S Talwalkar · 2018
Cited alongside, same era.
BadNets: Evaluating Backdooring Attacks on Deep Neural Networks
T. Gu, K. Liu, B. Dolan-Gavitt, and S. Garg · 2019
Cited alongside, same era.
DLIME: A deterministic local interpretable model-agnostic explanations approach for computer-aided diagnosis systems
Muhammad Rehman Zafar and Naimul Mefraz Khan · 2019
Cited alongside, same era.
Yi-Shan Lin, Wen-Chuan Lee, and Z. Berkay Celik · 2020
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Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
Christoph Molnar · 2020
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A modified perturbed sampling method for local interpretable model-agnostic explanation
Sheng Shi, Xinfeng Zhang, and Wei Fan · 2020
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How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations
Dylan Slack, Sophie Hilgard, Sameer Singh, and Himabindu Lakkaraju · 2020
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Explaining image classifiers using statistical fault localization
Youcheng Sun, Hana Chockler, Xiaowei Huang, and Daniel Kroening · 2020
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Interval Change-Point Detection for Runtime Probabilistic Model Checking
Xingyu Zhao, Radu Calinescu, Simos Gerasimou, Valentin Robu, and David Flynn · 2020
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