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EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy

Aug 10, 2020
Jonas Rauber, Matthias Bethge, Wieland Brendel


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Fast Differentiable Clipping-Aware Normalization and Rescaling

Jul 15, 2020
Jonas Rauber, Matthias Bethge


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Modeling patterns of smartphone usage and their relationship to cognitive health

Nov 13, 2019
Jonas Rauber, Emily B. Fox, Leon A. Gatys

* Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract 

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Accurate, reliable and fast robustness evaluation

Jul 01, 2019
Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge


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Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks

Mar 27, 2019
Francesco Croce, Jonas Rauber, Matthias Hein


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On Evaluating Adversarial Robustness

Feb 20, 2019
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, Alexey Kurakin

* Living document; source available at https://github.com/evaluating-adversarial-robustness/adv-eval-paper/ 

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Towards the first adversarially robust neural network model on MNIST

Sep 20, 2018
Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel


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Generalisation in humans and deep neural networks

Aug 27, 2018
Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber, Heiko H. Schuett, Matthias Bethge, Felix A. Wichmann

* Submitted to NIPS 2018. 26 pages, 14 figures, 1 table. Supersedes and greatly extends arXiv:1706.06969 

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Adversarial Vision Challenge

Aug 06, 2018
Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge

* https://www.crowdai.org/challenges/adversarial-vision-challenge 

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Technical Report on the CleverHans v2.1.0 Adversarial Examples Library

Jun 27, 2018
Nicolas Papernot, Fartash Faghri, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Alexey Kurakin, Cihang Xie, Yash Sharma, Tom Brown, Aurko Roy, Alexander Matyasko, Vahid Behzadan, Karen Hambardzumyan, Zhishuai Zhang, Yi-Lin Juang, Zhi Li, Ryan Sheatsley, Abhibhav Garg, Jonathan Uesato, Willi Gierke, Yinpeng Dong, David Berthelot, Paul Hendricks, Jonas Rauber, Rujun Long, Patrick McDaniel

* Technical report for https://github.com/tensorflow/cleverhans 

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Foolbox: A Python toolbox to benchmark the robustness of machine learning models

Mar 20, 2018
Jonas Rauber, Wieland Brendel, Matthias Bethge

* Code and examples available at https://github.com/bethgelab/foolbox and documentation available at http://foolbox.readthedocs.io 

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Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

Feb 16, 2018
Wieland Brendel, Jonas Rauber, Matthias Bethge

* Published as a conference paper at the Sixth International Conference on Learning Representations (ICLR 2018) https://openreview.net/forum?id=SyZI0GWCZ 

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Comparing deep neural networks against humans: object recognition when the signal gets weaker

Jun 21, 2017
Robert Geirhos, David H. J. Janssen, Heiko H. Schütt, Jonas Rauber, Matthias Bethge, Felix A. Wichmann


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