Publications associées à Random matrix methods for high-dimensional machine learning models

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

Random matrix methods for high-dimensional machine learning models

Graph Chatbot

Chattez avec Graph Search

Machine Learning Security in Industry: A Quantitative Survey

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

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

Validation of semi-analytical, semi-empirical covariance matrices for two-point correlation function for early DESI data

Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits

Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Machine Learning Security in Industry: A Quantitative Survey

Source-Free Open-Set Domain Adaptation for Histopathological Images via Distilling Self-Supervised Vision Transformer

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

Validation of semi-analytical, semi-empirical covariance matrices for two-point correlation function for early DESI data

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

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