579 citations · 591 across the 4 of their papers we have counts for
6 papers
EagerPy: Writing Code That Works Natively with PyTorch, TensorFlow, JAX, and NumPy
Jonas Rauber, Matthias Bethge, Wieland Brendel
EagerPy is a Python framework that lets you write code that automatically works natively with PyTorch, TensorFlow, JAX, and NumPy. Library developers no longer need to choose betwe…
Fast Differentiable Clipping-Aware Normalization and Rescaling
Jonas Rauber, Matthias Bethge
Rescaling a vector to a desired length is a common operation in many areas such as data science and machine learning. When the rescaled perturbation $η\vec…
Modeling patterns of smartphone usage and their relationship to cognitive health
Jonas Rauber, Emily B. Fox, Leon A. Gatys
The ubiquity of smartphone usage in many people's lives make it a rich source of information about a person's mental and cognitive state. In this work we analyze 12 weeks of phone…
Accurate, reliable and fast robustness evaluation
Wieland Brendel, Jonas Rauber, Matthias Kümmerer +2
Throughout the past five years, the susceptibility of neural networks to minimal adversarial perturbations has moved from a peculiar phenomenon to a core issue in Deep Learning. De…
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks
Francesco Croce, Jonas Rauber, Matthias Hein
Modern neural networks are highly non-robust against adversarial manipulation. A significant amount of work has been invested in techniques to compute lower bounds on robustness th…
On Evaluating Adversarial Robustness
Nicholas Carlini, Anish Athalye, Nicolas Papernot +6
Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that wi…