Publication

Loss landscape and symmetries in Neural Networks

Publications associées (351)

Graph Chatbot

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.

Connectez-vous pour utiliser Chat avec Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Loss landscape and symmetries in Neural Networks

Graph Chatbot

Chattez avec Graph Search

Robust machine learning for neuroscientific inference

Topics in statistical physics of high-dimensional machine learning

Random matrix methods for high-dimensional machine learning models

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Incorporating Projective Geometry into Deep Learning

Partial discharge localization in power transformer tanks using machine learning methods

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Incorporating Projective Geometry into Deep Learning

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Random matrix methods for high-dimensional machine learning models

Topics in statistical physics of high-dimensional machine learning

Robust machine learning for neuroscientific inference

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Partial discharge localization in power transformer tanks using machine learning methods