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We give a new algorithm for approximating the Discrete Fourier transform of an approximately sparse signal that has been corrupted by worst-case $L_0$ noise, namely a bounded number of coordinates of the signal have been corrupted arbitrarily.
The mnist database of handwritten digits
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Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
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Iterative hard thresholding for compressed sensing
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Cosamp: Iterative signal recovery from incomplete and inaccurate samples
Deanna Needell and Joel A Tropp · 2008
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Simple and practical algorithm for sparse fourier transform
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Approximation algorithms for model-based compressive sensing
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Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, and Michael K Reiter · 2016
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Arturs Backurs, Piotr Indyk, and Ludwig Schmidt · 2017
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Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, and Justin Gilmer · 2017
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Robust physical-world attacks on machine learning models
Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, and Dawn Song · 2017
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The robust manifold defense: Adversarial training using generative models
Andrew Ilyas, Ajil Jalal, Eirini Asteri, Constantinos Daskalakis, and Alexandros G. Dimakis · 2017
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Towards deep learning models resistant to adversarial attacks
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Dan Boneh, and Patrick McDaniel · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
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Defense-gan: Protecting classifiers against adversarial attacks using generative models
Pouya Samangouei, Maya Kabkab, and Rama Chellappa · 2018
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