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A Representer Theorem for Deep Neural Networks

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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

A Representer Theorem for Deep Neural Networks

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Chattez avec Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

On the number of regions of piecewise linear neural networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

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

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

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

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

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

On the number of regions of piecewise linear neural networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

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

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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