Publications associées à Deep Learning Generalization with Limited and Noisy Labels

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

Deep Learning Generalization with Limited and Noisy Labels

Graph Chatbot

Chattez avec Graph Search

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

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

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Towards learning-based denoising of light fields

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

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

Improved calorimetric particle identification in NA62 using machine learning techniques

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations

Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions

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

Improved calorimetric particle identification in NA62 using machine learning techniques

Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions

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

Towards Robust Monitoring of the Laser Powder Bed Fusion Process based on Acoustic Emission combined with Machine Learning Solutions

Towards learning-based denoising of light fields

Learnable Wavelet Transform and Domain Adversarial Learning for Enhanced Bearing Fault Diagnosis

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

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

The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations