Publications associées à Loss landscape and symmetries in Neural Networks

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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

Loss landscape and symmetries in Neural Networks

Graph Chatbot

Chattez avec Graph Search

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Dense Image-based Predictions for Comics Analysis

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

Modeling Visual Impairments with Artificial Neural Networks: a Review

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

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

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

Incremental Learning in Diagonal Linear Networks

Modeling Visual Impairments with Artificial Neural Networks: a Review

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

Combining biophysical models and machine learning to optimize implant geometry and stimulation protocol for intraneural electrodes

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Dense Image-based Predictions for Comics Analysis

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

Scale-Aware Test-Time Click Adaptation for Pulmonary Nodule and Mass Segmentation

Self-Supervised Bayesian representation learning of acoustic emissions from laser powder bed Fusion process for in-situ monitoring

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

Incremental Learning in Diagonal Linear Networks