Jonas Rauber

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

EagerPy is a Python framework that lets you write code that automaticall...
08/10/2020 ∙ by Jonas Rauber, et al. ∙ 30 ∙ share

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Fast Differentiable Clipping-Aware Normalization and Rescaling

Rescaling a vector δ⃗∈ℝ^n to a desired length is a common operation in m...
07/15/2020 ∙ by Jonas Rauber, et al. ∙ 6 ∙ share

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Modeling patterns of smartphone usage and their relationship to cognitive health

The ubiquity of smartphone usage in many people's lives make it a rich s...
11/13/2019 ∙ by Jonas Rauber, et al. ∙ 0 ∙ share

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Accurate, reliable and fast robustness evaluation

Throughout the past five years, the susceptibility of neural networks to...
07/01/2019 ∙ by Wieland Brendel, et al. ∙ 3 ∙ share

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

Modern neural networks are highly non-robust against adversarial manipul...
03/27/2019 ∙ by Francesco Croce, et al. ∙ 0 ∙ share

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On Evaluating Adversarial Robustness

Correctly evaluating defenses against adversarial examples has proven to...
02/18/2019 ∙ by Nicholas Carlini, et al. ∙ 10 ∙ share

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Generalisation in humans and deep neural networks

We compare the robustness of humans and current convolutional deep neura...
08/27/2018 ∙ by Robert Geirhos, et al. ∙ 4 ∙ share

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Adversarial Vision Challenge

The NIPS 2018 Adversarial Vision Challenge is a competition to facilitat...
08/06/2018 ∙ by Wieland Brendel, et al. ∙ 4 ∙ share

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Robust Perception through Analysis by Synthesis

The intriguing susceptibility of deep neural networks to minimal input p...
05/23/2018 ∙ by Lukas Schott, et al. ∙ 0 ∙ share

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

Many machine learning algorithms are vulnerable to almost imperceptible ...
12/12/2017 ∙ by Wieland Brendel, et al. ∙ 0 ∙ share

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

Even todays most advanced machine learning models are easily fooled by a...
07/13/2017 ∙ by Jonas Rauber, et al. ∙ 0 ∙ share

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

Human visual object recognition is typically rapid and seemingly effortl...
06/21/2017 ∙ by Robert Geirhos, et al. ∙ 0 ∙ share

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Jonas Rauber

Featured Co-authors

Matthias Hein

Ian Goodfellow

Matthias Bethge

Nicolas Papernot

Nicholas Carlini

Aleksander Madry

Emily B. Fox

Wieland Brendel

Dimitris Tsipras

Marcel Salathe

Francesco Croce

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

Fast Differentiable Clipping-Aware Normalization and Rescaling

Modeling patterns of smartphone usage and their relationship to cognitive health

Accurate, reliable and fast robustness evaluation

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

On Evaluating Adversarial Robustness

Generalisation in humans and deep neural networks

Adversarial Vision Challenge

Robust Perception through Analysis by Synthesis

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

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

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