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Learning Multi-Label Aerial Image Classification Under Label Noise: A Regularization Approach Using Word Embeddings

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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

Learning Multi-Label Aerial Image Classification Under Label Noise: A Regularization Approach Using Word Embeddings

Graph Chatbot

Chattez avec Graph Search

Random matrix methods for high-dimensional machine learning models

Aggregating Spatial and Photometric Context for Photometric Stereo

Topics in statistical physics of high-dimensional machine learning

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

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

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

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

Random matrix methods for high-dimensional machine learning models

Aggregating Spatial and Photometric Context for Photometric Stereo

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

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Topics in statistical physics of high-dimensional machine learning

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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