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Neural network training for cross-protocol radiomic feature standardization in computed tomography

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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

Neural network training for cross-protocol radiomic feature standardization in computed tomography

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Chattez avec Graph Search

Aggregating Spatial and Photometric Context for Photometric Stereo

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

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

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

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

Random matrix methods for high-dimensional machine learning models

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Aggregating Spatial and Photometric Context for Photometric Stereo

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

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

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

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Random matrix methods for high-dimensional machine learning models

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts