Publications associées à Model-based Sketching and Recovery with Expanders

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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

Model-based Sketching and Recovery with Expanders

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Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

Principles of Network Plasticity in Neocortical Microcircuits

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Approximating Relu Networks By Single-Spike Computation

Space-Efficient Representations of Graphs

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Principles of Network Plasticity in Neocortical Microcircuits

Variational Methods For Continuous-Domain Inverse Problems: the Quest for the Sparsest Solution

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Space-Efficient Representations of Graphs

Approximating Relu Networks By Single-Spike Computation

(S)GD over Diagonal Linear Networks: Implicit Regularisation, Large Stepsizes and Edge of Stability

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition