Jonas Rauber

According to our database, Jonas Rauber authored at least 15 papers between 2017 and 2021.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2021

Advances in Reliably Evaluating and Improving Adversarial Robustness.

Jonas Rauber

PhD thesis, 2021

2020

Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX.

Jonas Rauber, Roland Zimmermann, Matthias Bethge, Wieland Brendel

J. Open Source Softw., 2020

Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks.

Francesco Croce, Jonas Rauber, Matthias Hein

Int. J. Comput. Vis., 2020

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

Jonas Rauber, Matthias Bethge, Wieland Brendel

CoRR, 2020

Fast Differentiable Clipping-Aware Normalization and Rescaling.

Jonas Rauber, Matthias Bethge

CoRR, 2020

2019

Modeling patterns of smartphone usage and their relationship to cognitive health.

Jonas Rauber, Emily B. Fox, Leon A. Gatys

CoRR, 2019

On Evaluating Adversarial Robustness.

Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin

CoRR, 2019

Accurate, reliable and fast robustness evaluation.

Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Towards the first adversarially robust neural network model on MNIST.

Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Adversarial Vision Challenge.

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

CoRR, 2018

Robust Perception through Analysis by Synthesis.

Lukas Schott, Jonas Rauber, Wieland Brendel, Matthias Bethge

CoRR, 2018

Generalisation in humans and deep neural networks.

Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, Felix A. Wichmann

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

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

Wieland Brendel, Jonas Rauber, Matthias Bethge

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models.

Jonas Rauber, Wieland Brendel, Matthias Bethge

CoRR, 2017

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

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

CoRR, 2017