Publications associées à STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

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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

STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization

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Residual-based attention in physics-informed neural networks

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

On the Generalization of Stochastic Gradient Descent with Momentum

Universal and adaptive methods for robust stochastic optimization

Contextual Stochastic Bilevel Optimization

Phenomenological theory of variational quantum ground-state preparation

Fast Adversarial Training With Adaptive Step Size

The statistical complexity of early-stopped mirror descent

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

ADAGRAD Avoids Saddle Points

Universal and adaptive methods for robust stochastic optimization

Phenomenological theory of variational quantum ground-state preparation

Residual-based attention in physics-informed neural networks

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

On the Generalization of Stochastic Gradient Descent with Momentum

Contextual Stochastic Bilevel Optimization

Fast Adversarial Training With Adaptive Step Size

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

The statistical complexity of early-stopped mirror descent

ADAGRAD Avoids Saddle Points