Publications associées à Use of Transient Measurements for Static Real-Time 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

Use of Transient Measurements for Static Real-Time Optimization

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Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Residual-based attention in physics-informed neural networks

Scalable constrained optimization

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Mesh-based topology, shape and sizing optimization of ribbed plates

Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Training Efficient Controllers via Analytic Policy Gradient

A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Residual-based attention in physics-informed neural networks

Scalable constrained optimization

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Mesh-based topology, shape and sizing optimization of ribbed plates

Universal and adaptive methods for robust stochastic optimization

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Training Efficient Controllers via Analytic Policy Gradient

A comprehensive review of digital twin-part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives

Data-Driven Control and Optimization under Noisy and Uncertain Conditions