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Recurrent Convolutional Neural Networks for Scene Labeling

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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

Recurrent Convolutional Neural Networks for Scene Labeling

Graph Chatbot

Chattez avec Graph Search

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

Random matrix methods for high-dimensional machine learning models

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

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

On the number of regions of piecewise linear neural networks

Aggregating Spatial and Photometric Context for Photometric Stereo

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

Random matrix methods for high-dimensional machine learning models

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

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

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

On the number of regions of piecewise linear neural networks

Aggregating Spatial and Photometric Context for Photometric Stereo