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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

Algorithme d'apprentissage incrémental

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Topics in statistical physics of high-dimensional machine learning

Understanding generalization and robustness in modern deep learning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Residual-based attention in physics-informed neural networks

Predicting the long-term collective behaviour of fish pairs with deep learning

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Stochastic Simulations and Machine Learning Modeling to Predict Bedload Transport and Bed Topography

TTool: A Supervised Artificial Intelligence-Assisted Visual Pose Detector for Tool Heads in Augmented Reality Woodworking

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Efficient local linearity regularization to overcome catastrophic overfitting

Mapping drivers of tropical forest loss with satellite image time series and machine learning

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Topics in statistical physics of high-dimensional machine learning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Stochastic Simulations and Machine Learning Modeling to Predict Bedload Transport and Bed Topography

Understanding generalization and robustness in modern deep learning

TTool: A Supervised Artificial Intelligence-Assisted Visual Pose Detector for Tool Heads in Augmented Reality Woodworking

Residual-based attention in physics-informed neural networks

Predicting the long-term collective behaviour of fish pairs with deep learning

Efficient local linearity regularization to overcome catastrophic overfitting