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Towards Understanding Sharpness-Aware Minimization

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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

Towards Understanding Sharpness-Aware Minimization

Graph Chatbot

Chattez avec Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Understanding generalization and robustness in modern deep learning

Deep Learning Generalization with Limited and Noisy Labels

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Incremental Learning in Diagonal Linear Networks

Polynomial-time universality and limitations of deep learning

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Masked Training of Neural Networks with Partial Gradients

Theory of Deep Learning: Neural Tangent Kernel and Beyond

Understanding generalization and robustness in modern deep learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Polynomial-time universality and limitations of deep learning

Deep Learning Generalization with Limited and Noisy Labels

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Incremental Learning in Diagonal Linear Networks

Masked Training of Neural Networks with Partial Gradients

Theory of Deep Learning: Neural Tangent Kernel and Beyond