Publications associées à Disentangling feature and lazy training in deep neural networks

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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

Disentangling feature and lazy training in deep neural networks

Graph Chatbot

Chattez avec Graph Search

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

Euclid: Identification of asteroid streaks in simulated images using deep learning

Stereo Confidence Estimation via Locally Adaptive Fusion and Knowledge Distillation

Linear Complexity Self-Attention With 3rd Order Polynomials

Explicit representations for Banach subspaces of Lizorkin distributions

On the symmetries in the dynamics of wide two-layer neural networks

A Comparison of Residual-based Methods on Fault Detection

Theory of Deep Learning: Neural Tangent Kernel and Beyond

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Improving the Training of Compact Neural Networks for Visual Recognition

Euclid: Identification of asteroid streaks in simulated images using deep learning

Improving the Training of Compact Neural Networks for Visual Recognition

Linear Complexity Self-Attention With 3rd Order Polynomials

A Comparison of Residual-based Methods on Fault Detection

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Theory of Deep Learning: Neural Tangent Kernel and Beyond

On the symmetries in the dynamics of wide two-layer neural networks

Stereo Confidence Estimation via Locally Adaptive Fusion and Knowledge Distillation

Explicit representations for Banach subspaces of Lizorkin distributions

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