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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

Perceptron

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Boosting likelihood learning with event reweighting

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

Exploring neural networks and their potential for species distribution modeling

A new variable shape parameter strategy for RBF approximation using neural networks

Enhancing Epileptic Seizure Detection with EEG Feature Embeddings

Privacy-preserving federated neural network training and inference

Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme

Boosting likelihood learning with event reweighting

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

Exploring neural networks and their potential for species distribution modeling

Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis

Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme

A new variable shape parameter strategy for RBF approximation using neural networks

Enhancing Epileptic Seizure Detection with EEG Feature Embeddings

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Privacy-preserving federated neural network training and inference

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions