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ORCIDJonas Rauber
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2020 – today
- 2021
[b1]Jonas Rauber:
Advances in Reliably Evaluating and Improving Adversarial Robustness. University of Tübingen, Germany, 2021- 2020
- [j2]Francesco Croce, Jonas Rauber, Matthias Hein:
Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks. Int. J. Comput. Vis. 128(4): 1028-1046 (2020) - [j1]Jonas Rauber
, Roland Zimmermann, Matthias Bethge, Wieland Brendel:
Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. J. Open Source Softw. 5(53): 2607 (2020) - [i12]Jonas Rauber, Matthias Bethge:
Fast Differentiable Clipping-Aware Normalization and Rescaling. CoRR abs/2007.07677 (2020) - [i11]Jonas Rauber, Matthias Bethge, Wieland Brendel:
EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy. CoRR abs/2008.04175 (2020)
2010 – 2019
- 2019
- [c4]Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel:
Towards the first adversarially robust neural network model on MNIST. ICLR (Poster) 2019 - [c3]Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge:
Accurate, reliable and fast robustness evaluation. NeurIPS 2019: 12841-12851 - [i10]Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin:
On Evaluating Adversarial Robustness. CoRR abs/1902.06705 (2019) - [i9]Francesco Croce, Jonas Rauber, Matthias Hein:
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks. CoRR abs/1903.11359 (2019) - [i8]Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge:
Accurate, reliable and fast robustness evaluation. CoRR abs/1907.01003 (2019) - [i7]Jonas Rauber, Emily B. Fox, Leon A. Gatys:
Modeling patterns of smartphone usage and their relationship to cognitive health. CoRR abs/1911.05683 (2019) - 2018
- [c2]Wieland Brendel, Jonas Rauber, Matthias Bethge:
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. ICLR (Poster) 2018 - [c1]Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, Felix A. Wichmann:
Generalisation in humans and deep neural networks. NeurIPS 2018: 7549-7561 - [i6]Lukas Schott, Jonas Rauber, Wieland Brendel, Matthias Bethge:
Robust Perception through Analysis by Synthesis. CoRR abs/1805.09190 (2018) - [i5]Wieland Brendel, Jonas Rauber, Alexey Kurakin, Nicolas Papernot, Behar Veliqi, Marcel Salathé, Sharada P. Mohanty, Matthias Bethge:
Adversarial Vision Challenge. CoRR abs/1808.01976 (2018) - [i4]Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, Felix A. Wichmann:
Generalisation in humans and deep neural networks. CoRR abs/1808.08750 (2018) - 2017
- [i3]Robert Geirhos, David H. J. Janssen, Heiko H. Schütt, Jonas Rauber, Matthias Bethge, Felix A. Wichmann:
Comparing deep neural networks against humans: object recognition when the signal gets weaker. CoRR abs/1706.06969 (2017) - [i2]Jonas Rauber, Wieland Brendel, Matthias Bethge:
Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models. CoRR abs/1707.04131 (2017) - [i1]Wieland Brendel, Jonas Rauber, Matthias Bethge:
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models. CoRR abs/1712.04248 (2017)
Coauthor Index
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