Yann Traonmilin

According to our database, Yann Traonmilin authored at least 28 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Batch-Less Stochastic Gradient Descent for Compressive Learning of Deep Regularization for Image Denoising.

Hui Shi, Yann Traonmilin, Jean-François Aujol

J. Math. Imaging Vis., August, 2024

On Strong Basins of Attractions for Non-convex Sparse Spike Estimation: Upper and Lower Bounds.

Yann Traonmilin, Jean-François Aujol, Pierre-Jean Bénard, Arthur Leclaire

J. Math. Imaging Vis., January, 2024

Adaptive Parameter Selection for Gradient-Sparse Plus Low Patch-Rank Recovery: Application to Image Decomposition.

Antoine Guennec, Jean-François Aujol, Yann Traonmilin

Proceedings of the 32nd European Signal Processing Conference, 2024

Projected Block Coordinate Descent for Sparse Spike Estimation.

Pierre-Jean Bénard, Yann Traonmilin, Jean-François Aujol

Proceedings of the 32nd European Signal Processing Conference, 2024

2023

Compressive Learning of Deep Regularization for Denoising.

Hui Shi, Yann Traonmilin, Jean-François Aujol

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2023

2022

Compressive Learning for Patch-Based Image Denoising.

Hui Shi, Yann Traonmilin, Jean-François Aujol

SIAM J. Imaging Sci., September, 2022

Fast off-the-grid sparse recovery with over-parametrized projected gradient descent.

Pierre-Jean Bénard, Yann Traonmilin, Jean-François Aujol

Proceedings of the 30th European Signal Processing Conference, 2022

Piecewise linear prediction model for action tracking in sports.

Axel Baldanza, Jean-François Aujol, Yann Traonmilin, François Alary

Proceedings of the 30th European Signal Processing Conference, 2022

2021

Sketched Learning for Image Denoising.

Hui Shi, Yann Traonmilin, Jean-François Aujol

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2021

Sur la performance des méthodes convexes et non-convexes de reconstruction de modèles de faible dimension en science des données.

Yann Traonmilin

, 2021

2020

Projected Gradient Descent for Non-Convex Sparse Spike Estimation.

Yann Traonmilin, Jean-François Aujol, Arthur Leclaire

IEEE Signal Process. Lett., 2020

An algorithm for non-convex off-the-grid sparse spike estimation with a minimum separation constraint.

Yann Traonmilin, Jean-François Aujol, Arthur Leclaire

CoRR, 2020

The basins of attraction of the global minimizers of non-convex inverse problems with low-dimensional models in infinite dimension.

Yann Traonmilin, Jean-François Aujol, Arthur Leclaire

CoRR, 2020

Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling.

Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin

CoRR, 2020

2018

The basins of attraction of the global minimizers of the non-convex sparse spikes estimation problem.

Yann Traonmilin, Jean-François Aujol

CoRR, 2018

Is the 1-norm the best convex sparse regularization?

Yann Traonmilin, Samuel Vaiter, Rémi Gribonval

CoRR, 2018

Optimality of 1-norm regularization among weighted 1-norms for sparse recovery: a case study on how to find optimal regularizations.

Yann Traonmilin, Samuel Vaiter

CoRR, 2018

2017

Compressed sensing in Hilbert spaces.

Yann Traonmilin, Gilles Puy, Rémi Gribonval, Mike E. Davies

CoRR, 2017

Compressive Statistical Learning with Random Feature Moments.

Rémi Gribonval, Gilles Blanchard, Nicolas Keriven, Yann Traonmilin

CoRR, 2017

Compressive K-means.

Nicolas Keriven, Nicolas Tremblay, Yann Traonmilin, Rémi Gribonval

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Phase unmixing: Multichannel source separation with magnitude constraints.

Antoine Deleforge, Yann Traonmilin

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016

A framework for low-complexity signal recovery and its application to structured sparsity.

Yann Traonmilin, Rémi Gribonval

Proceedings of the 2016 IEEE Information Theory Workshop, 2016

2015

Robust Multi-image Processing with Optimal Sparse Regularization.

Yann Traonmilin, Saïd Ladjal, Andrés Almansa

J. Math. Imaging Vis., 2015

Stable recovery of low-dimensional cones in Hilbert spaces: One RIP to rule them all.

Yann Traonmilin, Rémi Gribonval

CoRR, 2015

2014

Relations entre le modèle d'image et le nombre de mesures pour une super-résolution fidèle. (Relations between image model and number of measures for a high fidelity super-resolution).

Yann Traonmilin

PhD thesis, 2014

Simultaneous High Dynamic Range and Superresolution Imaging without Regularization.

Yann Traonmilin, Cecilia Aguerrebere

SIAM J. Imaging Sci., 2014

2013

Outlier Removal Power of the L1-Norm Super-Resolution.

Yann Traonmilin, Saïd Ladjal, Andrés Almansa

Proceedings of the Scale Space and Variational Methods in Computer Vision, 2013

2012

On the amount of regularization for super-resolution interpolation.

Yann Traonmilin, Saïd Ladjal, Andrés Almansa

Proceedings of the 20th European Signal Processing Conference, 2012