Publications associées à Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov, Scott William Pesme

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

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Partial discharge localization in power transformer tanks using machine learning methods

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

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

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

Deep Learning Generalization with Limited and Noisy Labels

Polynomial-time universality and limitations of deep learning

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

TOPO-Loss for continuity-preserving crack detection using deep learning

Multivariate time series forecasting for freeway networks

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

TOPO-Loss for continuity-preserving crack detection using deep learning

Partial discharge localization in power transformer tanks using machine learning methods

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

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

Deep Learning Generalization with Limited and Noisy Labels

Polynomial-time universality and limitations of deep learning

Multivariate time series forecasting for freeway networks

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design