Publications associées à Deep Generative Models and Applications

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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

Deep Generative Models and Applications

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Chattez avec Graph Search

Incremental Learning in Diagonal Linear Networks

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

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

Dense Image-based Predictions for Comics Analysis

Neural ADMIXTURE for rapid genomic clustering

End-to-end kernel learning via generative random Fourier features

Improved calorimetric particle identification in NA62 using machine learning techniques

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

Incremental Learning in Diagonal Linear Networks

Neural ADMIXTURE for rapid genomic clustering

End-to-end kernel learning via generative random Fourier features

On the Privacy-Robustness-Utility Trilemma in Distributed Learning

Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups

Smart filter aided domain adversarial neural network for fault diagnosis in noisy industrial scenarios

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

Dense Image-based Predictions for Comics Analysis

Improved calorimetric particle identification in NA62 using machine learning techniques

Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning