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Locality defeats the curse of dimensionality in convolutional teacher-student scenarios

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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

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios

Graph Chatbot

Chattez avec Graph Search

Topics in statistical physics of high-dimensional machine learning

Machine Learning for Modeling Stock Returns

Random matrix methods for high-dimensional machine learning models

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

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

Aggregating Spatial and Photometric Context for Photometric Stereo

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

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

Topics in statistical physics of high-dimensional machine learning

Machine Learning for Modeling Stock Returns

Random matrix methods for high-dimensional machine learning models

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Aggregating Spatial and Photometric Context for Photometric Stereo

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

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

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