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Importance Sampling Tree for Large-scale Empirical Expectation

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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

Importance Sampling Tree for Large-scale Empirical Expectation

Graph Chatbot

Chattez avec Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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

Deep Learning Generalization with Limited and Noisy Labels

ReLU Neural Network Galerkin BEM

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions

Monte Carlo Estimators for Differential Light Transport

Tree in Tree: from Decision trees to Decision Graphs

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Deep Learning Generalization with Limited and Noisy Labels

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

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Monte Carlo Estimators for Differential Light Transport

ReLU Neural Network Galerkin BEM

Knowledge Distillation for 6D Pose Estimation by Aligning Distributions of Local Predictions

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Tree in Tree: from Decision trees to Decision Graphs