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Capsule networks, but not convolutional networks explain global configurational visual effects

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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

Capsule networks, but not convolutional networks explain global configurational visual effects

Graph Chatbot

Chattez avec Graph Search

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Probing and modulating inter-areal coupling in the cortical visual motion processing pathway with non-invasive brain stimulation

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

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Random matrix methods for high-dimensional machine learning models

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Probing and modulating inter-areal coupling in the cortical visual motion processing pathway with non-invasive brain stimulation

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

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Random matrix methods for high-dimensional machine learning models

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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