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An emerging problem in trustworthy machine learning is to train models that produce robust interpretations for their predictions.
A new measure of rank correlation
Maurice G Kendall · 1938
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Improving generalization performance using double backpropagation
Harris Drucker and Yann LeCun · 1992
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Optimization by Vector Space Methods
David G. Luenberger · 1997
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The mnist database of handwritten digits, 1998
Yann LeCun, Corinna Cortes, and Christopher J.C. Burges · 1998
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A visual vocabulary for flower classification
M-E Nilsback and Andrew Zisserman · 2006
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Robust Optimization
A. Ben-Tal, L. El Ghaoui, and A.S. Nemirovski · 2009
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Variational analysis
R Tyrrell Rockafellar and Roger J-B Wets · 2009
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
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Tensorflow: A system for large-scale machine learning
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Interpretation of neural networks is fragile
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Adversarial robustness – theory and practice, 2018
Zico Kolter and Aleksander Madry · 2018
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Slavin Ross and Finale Doshi-Velez · 2018
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Certifying some distributional robustness with principled adversarial training
Aman Sinha, Hongseok Namkoong, and John C. Duchi · 2018
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and J. Zico Kolter · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Adversarial robustness as a prior for learned representations
Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, and Aleksander Madry · 2019
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Robustness may be at odds with accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
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