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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

Apprentissage automatique

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A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Sparse autoregressive neural networks for classical spin systems

Aggregating Spatial and Photometric Context for Photometric Stereo

Robust NAS under adversarial training: benchmark, theory, and beyond

Enabling Uncertainty Estimation in Iterative Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Robust NAS under adversarial training: benchmark, theory, and beyond

Aggregating Spatial and Photometric Context for Photometric Stereo

Sparse autoregressive neural networks for classical spin systems

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

Enabling Uncertainty Estimation in Iterative Neural Networks