Publications associées à Fast and Provable ADMM for Learning with Generative Priors

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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

Fast and Provable ADMM for Learning with Generative Priors

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Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Sparse autoregressive neural networks for classical spin systems

Understanding generalization and robustness in modern deep learning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Robust machine learning for neuroscientific inference

Gibbs sampling the posterior of neural networks

Machine Learning for Modeling Stock Returns

On the number of regions of piecewise linear neural networks

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Sparse autoregressive neural networks for classical spin systems

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Gibbs sampling the posterior of neural networks

Understanding generalization and robustness in modern deep learning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Robust machine learning for neuroscientific inference

On the number of regions of piecewise linear neural networks

Machine Learning for Modeling Stock Returns