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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

Machine à vecteurs de support

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Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis

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

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search

Spectral representation of EEG data using learned graphs with application to motor imagery decoding

Federated Learning under Covariate Shifts with Generalization Guarantees

Dynamic Modeling of an Origami Prismatic Joint

Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Two-level Data Augmentation for Calibrated Multi-view Detection

Reducibility bounds of objective functions over the integers

Classification of fall directions via wearable motion sensors

Dynamic Modeling of an Origami Prismatic Joint

Reducibility bounds of objective functions over the integers

Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis

Spectral representation of EEG data using learned graphs with application to motor imagery decoding

Multi-IVE: Privacy Enhancement of Multiple Soft-Biometrics in Face Embeddings

Two-level Data Augmentation for Calibrated Multi-view Detection

Classification of fall directions via wearable motion sensors

Federated Learning under Covariate Shifts with Generalization Guarantees

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

Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search