Publications associées à Inverse Analysis of Radiative Flux Maps for the Characterization of High Flux Sources

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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

Inverse Analysis of Radiative Flux Maps for the Characterization of High Flux Sources

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Towards Trustworthy Deep Learning for Image Reconstruction

Reversible photo-electrochemical device for solar hydrogen and power generation

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Efficient local linearity regularization to overcome catastrophic overfitting

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Understanding generalization and robustness in modern deep learning

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

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

On the Generalization of Stochastic Gradient Descent with Momentum

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

Reversible photo-electrochemical device for solar hydrogen and power generation

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Efficient local linearity regularization to overcome catastrophic overfitting

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Towards Trustworthy Deep Learning for Image Reconstruction

Understanding generalization and robustness in modern deep learning

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

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

On the Generalization of Stochastic Gradient Descent with Momentum

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability