Publications associées à Understanding and Improving Fast Adversarial Training

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Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

Understanding and Improving Fast Adversarial Training

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Chattez avec Graph Search

Estimating and Improving the Robustness of Attributions in Text

Deep Learning for Localized-Haptic Feedback in Tactile Surfaces

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Fast Adversarial Training With Adaptive Step Size

Probabilistic methods for neural combinatorial optimization

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Probabilistic methods for neural combinatorial optimization

Fast Adversarial Training With Adaptive Step Size

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Estimating and Improving the Robustness of Attributions in Text

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Deep Learning for Localized-Haptic Feedback in Tactile Surfaces

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective