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Interpretability of Deep Learning (DL) is a barrier to trustworthy AI.
Using genetic algorithms in engineering design optimization with non-linear constraints
David Powell and Michael M. Skolnick · 1993
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Zbigniew Michalewicz and Marc Schoenauer · 1996
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Weak convergence and optimal scaling of random walk metropolis algorithms
Andrew Gelman, Walter R Gilks, and Gareth O Roberts · 1997
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Estimation of small failure probabilities in high dimensions by subset simulation
Siu-Kui Au and James L Beck · 2001
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Siu-Kui Au and JL Beck · 2003
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A critical appraisal of reliability estimation procedures for high dimensions
Gerhart Iwo Schueller, Helmuth J Pradlwarter, and Phaedon-Stelios Koutsourelakis · 2004
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Multi-objective optimization using genetic algorithms: A tutorial
Abdullah Konak, David W Coit, and Alice E Smith · 2006
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Geometric insight into the challenges of solving high-dimensional reliability problems
Lambros S Katafygiotis and Konstantin M Zuev · 2008
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Sequential monte carlo for rare event estimation
Frédéric Cérou, Pierre Del Moral, Teddy Furon, and Arnaud Guyader · 2012
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Roulette-wheel selection via stochastic acceptance
Adam Lipowski and Dorota Lipowska · 2012
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Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Mcmc algorithms for subset simulation
Iason Papaioannou, Wolfgang Betz, Kilian Zwirglmaier, and Daniel Straub · 2015
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Striving for simplicity: The all convolutional net
J Springenberg, Alexey Dosovitskiy, Thomas Brox, and M Riedmiller · 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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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 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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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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User’s guide to correlation coefficients
Haldun Akoglu · 2018
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On the robustness of interpretability methods
David Alvarez-Melis and Tommi S Jaakkola · 2018
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Explanations can be manipulated and geometry is to blame
Ann-Kathrin Dombrowski, Maximillian Alber, Christopher Anders, Marcel Ackermann, Klaus-Robert Müller, and Pan Kessel · 2019
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Interpretation of Neural Networks Is Fragile
Interpretable Deep Learning under Fire
Xinyang Zhang, Ningfei Wang, Hua Shen, Shouling Ji, Xiapu Luo, and Ting Wang · 2020
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Explanations in autonomous driving: A survey
Daniel Omeiza, Helena Webb, Marina Jirotka, and Lars Kunze · 2021
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Statistically robust neural network classification
Benjie Wang, Stefan Webb, and Tom Rainforth · 2021
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A survey on neural network interpretability
Yu Zhang, Peter Tiňo, Aleš Leonardis, and Ke Tang · 2021
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Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles
Xingyu Zhao, Wei Huang, Alec Banks, Victoria Cox, David Flynn, Sven Schewe, and Xiaowei Huang · 2021
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BayLIME: Bayesian local interpretable model-agnostic explanations
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Fooling neural network interpretations via adversarial model manipulation
Juyeon Heo, Sunghwan Joo, and Taesup Moon · 2019
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The (Un)reliability of Saliency Methods
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A statistical approach to assessing neural network robustness
Stefan Webb, Tom Rainforth, Yee Whye Teh, and M. Pawan Kumar · 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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Interpreting and evaluating neural network robustness
Fuxun Yu, Zhuwei Qin, Chenchen Liu, Liang Zhao, Yanzhi Wang, and Xiang Chen · 2019
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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador Garcia, Sergio Gil-Lopez, Daniel Molina, Richard Benjamins, Raja Chatila, and Francisco Herrera · 2020
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Xingyu Zhao, Wei Huang, Xiaowei Huang, Valentin Robu, and David Flynn · 2021
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Openxai: Towards a transparent evaluation of model explanations
Chirag Agarwal, Eshika Saxena, Satyapriya Krishna, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju · 2022
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Framework for evaluating faithfulness of local explanations
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Towards robust explanations for deep neural networks
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Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
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Explainable artificial intelligence (xai) in deep learning-based medical image analysis
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Robust explanation constraints for neural networks
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Randomized adversarial training via taylor expansion
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