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Deep learning models have been the subject of study from various perspectives, for example, their training process, interpretation, generalization error, robustness to adversarial attacks, etc.
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A boundary tilting persepective on the phenomenon of adversarial examples
Thomas Tanay and Lewis Griffin · 2016
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Counterfactual explanations without opening the black box: Automated decisions and the GDPR
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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Predicting the generalization gap in deep networks with margin distributions
Yiding Jiang, Dilip Krishnan, Hossein Mobahi, and Samy Bengio · 2019
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A simple explanation for the existence of adversarial examples with small Hamming distance
Adi Shamir, Itay Safran, Eyal Ronen, and Orr Dunkelman · 2019
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Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio · 2018
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Empirical study of the topology and geometry of deep networks
Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, and Stefano Soatto · 2018
Cited alongside, same era.
With friends like these, who needs adversaries?
Saumya Jetley, Nicholas Lord, and Philip Torr · 2018
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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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Interpreting neural networks using flip points
Roozbeh Yousefzadeh and Dianne P O’Leary · 2019
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