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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...
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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...
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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...
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Accurate, reliable and fast robustness evaluation
Throughout the past five years, the susceptibility of neural networks to...
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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...
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On Evaluating Adversarial Robustness
Correctly evaluating defenses against adversarial examples has proven to...
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Generalisation in humans and deep neural networks
We compare the robustness of humans and current convolutional deep neura...
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Adversarial Vision Challenge
The NIPS 2018 Adversarial Vision Challenge is a competition to facilitat...
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Robust Perception through Analysis by Synthesis
The intriguing susceptibility of deep neural networks to minimal input p...
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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 ...
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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...
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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...
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