Publications associées à Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars

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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

Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars

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From Kernel Methods to Neural Networks: A Unifying Variational Formulation

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

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

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

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

Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Essays in Empirical Asset Pricing

Natural-Looking Adversarial Examples From Freehand Sketches

Improving the Training of Compact Neural Networks for Visual Recognition

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Spatial-Temporal Graph Attention Fuser for Calibration in IoT Air Pollution Monitoring Systems

Essays in Empirical Asset Pricing

Improving the Training of Compact Neural Networks for Visual Recognition

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

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

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

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

Deep Generative Models for Autonomous Driving: from Motion Forecasting to Realistic Image Synthesis

Natural-Looking Adversarial Examples From Freehand Sketches