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Batch normalization

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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

Batch normalization

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

On the Generalization of Stochastic Gradient Descent with Momentum

Incremental Learning in Diagonal Linear Networks

Deep Learning Generalization with Limited and Noisy Labels

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Benign Overfitting in Deep Neural Networks under Lazy Training

An exact mapping from ReLU networks to spiking neural networks

Supervised learning and inference of spiking neural networks with temporal coding

Exploring neural networks and their potential for species distribution modeling

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Deep Learning Generalization with Limited and Noisy Labels

Benign Overfitting in Deep Neural Networks under Lazy Training

Exploring neural networks and their potential for species distribution modeling

On the Generalization of Stochastic Gradient Descent with Momentum

Incremental Learning in Diagonal Linear Networks

An exact mapping from ReLU networks to spiking neural networks

Supervised learning and inference of spiking neural networks with temporal coding

From Kernel Methods to Neural Networks: A Unifying Variational Formulation