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Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer 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

Supply-Power-Constrained Cable Capacity Maximization Using Multi-Layer Neural Networks

Graph Chatbot

Chattez avec Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Generalization with Limited and Noisy Labels

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Predicting nonlinear optical scattering with physics-driven neural networks

An exact mapping from ReLU networks to spiking neural networks

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Transformer Models for Vision

Penalising the biases in norm regularisation enforces sparsity

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Deep Learning Generalization with Limited and Noisy Labels

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Predicting nonlinear optical scattering with physics-driven neural networks

Transformer Models for Vision

An exact mapping from ReLU networks to spiking neural networks

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Penalising the biases in norm regularisation enforces sparsity

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design