Publication

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

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

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Graph Chatbot

Chattez avec Graph Search

Sparse autoregressive neural networks for classical spin systems

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Topics in statistical physics of high-dimensional machine learning

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Gibbs sampling the posterior of neural networks

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

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

Sparse autoregressive neural networks for classical spin systems

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Topics in statistical physics of high-dimensional machine learning

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

Gibbs sampling the posterior of neural networks

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