Yann Traonmilin
@ytraonmilin.bsky.social
CNRS researcher at Institut de mathématiques de Bordeaux.
yanntraonmilin.perso.math.cnrs.fr
yanntraonmilin.perso.math.cnrs.fr
New (updated) preprint: Stochastic Orthogonal Regularization for deep projective priors, A. Joundi, YT, A. Newson. hal.science/hal-05069394v2
Geometry x deep priors x inverse problems = 👍
↓
Geometry x deep priors x inverse problems = 👍
↓
December 9, 2025 at 3:55 PM
New (updated) preprint: Stochastic Orthogonal Regularization for deep projective priors, A. Joundi, YT, A. Newson. hal.science/hal-05069394v2
Geometry x deep priors x inverse problems = 👍
↓
Geometry x deep priors x inverse problems = 👍
↓
Preprint: improved deterministic error bounds for post-training quantization for CNN architectures.
"On the impact of the parametrization of deep
convolutional neural networks on post-training
quantization", Samy Houache, J.-F. Aujol, Y.T.
hal.science/hal-04922698/
↓
"On the impact of the parametrization of deep
convolutional neural networks on post-training
quantization", Samy Houache, J.-F. Aujol, Y.T.
hal.science/hal-04922698/
↓
October 10, 2025 at 7:37 AM
Preprint: improved deterministic error bounds for post-training quantization for CNN architectures.
"On the impact of the parametrization of deep
convolutional neural networks on post-training
quantization", Samy Houache, J.-F. Aujol, Y.T.
hal.science/hal-04922698/
↓
"On the impact of the parametrization of deep
convolutional neural networks on post-training
quantization", Samy Houache, J.-F. Aujol, Y.T.
hal.science/hal-04922698/
↓
Congrats Dr Antoine Guennec who defended is PhD thesis yesterday on "Sparse models and deep priors for image decomposition into structure and
texture". He gave very nice contributions with modern methods on a challenging ill-posed problem.
antoineguennec.perso.math.cnrs.fr
texture". He gave very nice contributions with modern methods on a challenging ill-posed problem.
antoineguennec.perso.math.cnrs.fr
July 9, 2025 at 7:34 AM
Congrats Dr Antoine Guennec who defended is PhD thesis yesterday on "Sparse models and deep priors for image decomposition into structure and
texture". He gave very nice contributions with modern methods on a challenging ill-posed problem.
antoineguennec.perso.math.cnrs.fr
texture". He gave very nice contributions with modern methods on a challenging ill-posed problem.
antoineguennec.perso.math.cnrs.fr
...the (difficult) design of a regularizer for a complex combination of low dimensional models. Thanks to the PnP approach and DNN, we are able to explicitly design such regularizer in practice with promising results on natural images and inpainting problems.
February 19, 2025 at 7:17 AM
...the (difficult) design of a regularizer for a complex combination of low dimensional models. Thanks to the PnP approach and DNN, we are able to explicitly design such regularizer in practice with promising results on natural images and inpainting problems.
Accepted to SIIMS, congrats Antoine, next Step PhD defense!
"Joint structure-texture low dimensional modeling for image decomposition with a plug and play framework" (Guennec, Aujol, YT)
hal.science/hal-04648963v1
We describe how structure-texture decomposition is directly linked to ...
↓
"Joint structure-texture low dimensional modeling for image decomposition with a plug and play framework" (Guennec, Aujol, YT)
hal.science/hal-04648963v1
We describe how structure-texture decomposition is directly linked to ...
↓
February 19, 2025 at 7:17 AM
Accepted to SIIMS, congrats Antoine, next Step PhD defense!
"Joint structure-texture low dimensional modeling for image decomposition with a plug and play framework" (Guennec, Aujol, YT)
hal.science/hal-04648963v1
We describe how structure-texture decomposition is directly linked to ...
↓
"Joint structure-texture low dimensional modeling for image decomposition with a plug and play framework" (Guennec, Aujol, YT)
hal.science/hal-04648963v1
We describe how structure-texture decomposition is directly linked to ...
↓
Congrats to Ali Joundi for his paper accepted at SSVM2025
"Max-sparsity atomic autoencoders with application to inverse problems" with YT, A. Newson. hal.science/hal-04773954v1
↓
"Max-sparsity atomic autoencoders with application to inverse problems" with YT, A. Newson. hal.science/hal-04773954v1
↓
February 18, 2025 at 7:56 AM
Congrats to Ali Joundi for his paper accepted at SSVM2025
"Max-sparsity atomic autoencoders with application to inverse problems" with YT, A. Newson. hal.science/hal-04773954v1
↓
"Max-sparsity atomic autoencoders with application to inverse problems" with YT, A. Newson. hal.science/hal-04773954v1
↓
Updated preprint: "Towards optimal algorithms for the recovery of low-dimensional models with linear rates" (T., Aujol, Guennec) hal.science/hal-04725337
What is the "best" way to estimate elements of a low-dimensional model from a limited number of measurements ?
What is the "best" way to estimate elements of a low-dimensional model from a limited number of measurements ?
January 23, 2025 at 8:16 AM
Updated preprint: "Towards optimal algorithms for the recovery of low-dimensional models with linear rates" (T., Aujol, Guennec) hal.science/hal-04725337
What is the "best" way to estimate elements of a low-dimensional model from a limited number of measurements ?
What is the "best" way to estimate elements of a low-dimensional model from a limited number of measurements ?