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Generalization Properties of NAS under Activation and Skip Connection Search

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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

Generalization Properties of NAS under Activation and Skip Connection Search

Graph Chatbot

Chattez avec Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Robust NAS under adversarial training: benchmark, theory, and beyond

Topics in statistical physics of high-dimensional machine learning

Understanding generalization and robustness in modern deep learning

Random matrix methods for high-dimensional machine learning models

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

Residual-based attention in physics-informed neural networks

Explainable Face Verification via Feature-Guided Gradient Backpropagation

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

On the number of regions of piecewise linear neural networks

Residual-based attention in physics-informed neural networks

Explainable Face Verification via Feature-Guided Gradient Backpropagation

On the number of regions of piecewise linear neural networks

Topics in statistical physics of high-dimensional machine learning

Understanding generalization and robustness in modern deep learning

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

Random matrix methods for high-dimensional machine learning models

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

Robust NAS under adversarial training: benchmark, theory, and beyond