28 citations · 71 across the 18 of their papers we have counts for
25 papers
Layerwise Error Attribution for Fast and Robust Mixed-Precision Post-Training Quantization
Samy Houache, Yann Traonmilin, Jean-François Aujol
Mixed-precision post-training quantization is a network compression method that assigns bits layer by layer, under a global memory budget using a small calibration set. The main di…
Continuized Nesterov Momentum Achieves the Complexity in Smooth Nonconvex Optimization
Julien Hermant, Jean-François Aujol, Charles Dossal +3
For first-order optimization of non-convex functions with Lipschitz-continuous gradient and Hessian, the best-known complexity for reaching an -approximation of a stat…
Continuized Nesterov Acceleration for Non-Convex Optimization
Julien Hermant, Jean-François Aujol, Charles Dossal +2
In convex optimization, continuous-time counterparts have been a fruitful tool for analyzing momentum algorithms. Fewer such examples are available when the function to minimize is…
From sparse recovery to plug-and-play priors, understanding trade-offs for stable recovery with generalized projected gradient descent
Ali Joundi, Yann Traonmilin, Jean-François Aujol
We consider the problem of recovering an unknown low-dimensional vector from noisy, underdetermined observations. We focus on the Generalized Projected Gradient Descent (GPGD) fram…
Parameter-free structure-texture image decomposition by unrolling
Laura Girometti, Jean-François Aujol, Antoine Guennec +1
In this work, we propose a parameter-free and efficient method to tackle the structure-texture image decomposition problem. In particular, we present a neural network LPR-NET based…
On the impact of the parametrization of deep convolutional neural networks on post-training quantization
Samy Houache, Jean François Aujol, Yann Traonmilin
This paper introduces novel theoretical approximation bounds for the output of quantized neural networks, with a focus on convolutional neural networks (CNN). By considering layerw…
Gradient correlation is a key ingredient to accelerate SGD with momentum
Julien Hermant, Marien Renaud, Jean-François Aujol +2
Empirically, it has been observed that adding momentum to Stochastic Gradient Descent (SGD) accelerates the convergence of the algorithm. However, the literature has been rather pe…
Towards optimal algorithms for the recovery of low-dimensional models with linear rates
Yann Traonmilin, Jean François Aujol, Antoine Guennec
We consider the problem of recovering elements of a low-dimensional model from linear measurements. From signal and image processing to inverse problems in data science, this quest…
Strong Convergence of FISTA Iterates under H{ö}lderian and Quadratic Growth Conditions
Jean-François Aujol, Charles Dossal, Hippolyte Labarrière +1
Introduced by Beck and Teboulle, FISTA (for Fast Iterative Shrinkage-Thresholding Algorithm) is a first-order method widely used in convex optimization. Adapted from Nesterov's acc…
Heavy Ball Momentum for Non-Strongly Convex Optimization
Jean-François Aujol, Charles Dossal, Hippolyte Labarrière +1
When considering the minimization of a quadratic or strongly convex function, it is well known that first-order methods involving an inertial term weighted by a constant-in-time pa…
Projected Block Coordinate Descent for sparse spike estimation
Pierre-Jean Bénard, Yann Traonmilin, Jean François Aujol
We consider the problem of recovering off-the-grid spikes from linear measurements. The state of the art Over-Parametrized Continuous Orthogonal Matching Pursuit (OP-COMP) with Pro…
Batch-less stochastic gradient descent for compressive learning of deep regularization for image denoising
Hui Shi, Yann Traonmilin, J-F Aujol
We consider the problem of denoising with the help of prior information taken from a database of clean signals or images. Denoising with variational methods is very efficient if a…
Minimizing Quotient Regularization Model
Chao Wang, Jean-Francois Aujol, Guy Gilboa +1
Quotient regularization models (QRMs) are a class of powerful regularization techniques that have gained considerable attention in recent years, due to their ability to handle comp…
Parameter-Free FISTA by Adaptive Restart and Backtracking
Jean-François Aujol, Luca Calatroni, Charles Dossal +2
We consider a combined restarting and adaptive backtracking strategy for the popular Fast Iterative Shrinking-Thresholding Algorithm frequently employed for accelerating the conver…
Fast convergence of inertial dynamics with Hessian-driven damping under geometry assumptions
Jean-François Aujol, Charles Dossal, Văn Hào Hoàng +2
First-order optimization algorithms can be considered as a discretization of ordinary differential equations (ODEs) \cite{su2014differential}. In this perspective, studying the pro…
Fast off-the-grid sparse recovery with over-parametrized projected gradient descent
Pierre-Jean Bénard, Yann Traonmilin, Jean-François Aujol
We consider the problem of recovering off-the-grid spikes from Fourier measurements. Successful methods such as sliding Frank-Wolfe and continuous orthogonal matching pursuit (OMP)…
An algorithm for non-convex off-the-grid sparse spike estimation with a minimum separation constraint
Yann Traonmilin, Jean-François Aujol, Arhur Leclaire
Theoretical results show that sparse off-the-grid spikes can be estimated from (possibly compressive) Fourier measurements under a minimum separation assumption. We propose a pract…
PCA Reduced Gaussian Mixture Models with Applications in Superresolution
Johannes Hertrich, Dang Phoung Lan Nguyen, Jean-Fancois Aujol +4
Despite the rapid development of computational hardware, the treatment of large and high dimensional data sets is still a challenging problem. This paper provides a twofold contrib…
Projected gradient descent for non-convex sparse spike estimation
Yann Traonmilin, Jean-François Aujol, Arthur Leclaire
We propose a new algorithm for sparse spike estimation from Fourier measurements. Based on theoretical results on non-convex optimization techniques for off-the-grid sparse spike e…
LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net
Pierre Biasutti, Vincent Lepetit, Jean-François Aujol +2
We propose LU-Net -- for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as PointNet,…
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud
Pierre Biasutti, Aurélie Bugeau, Jean-François Aujol +1
This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud. The point cl…
The basins of attraction of the global minimizers of the non-convex sparse spike estimation problem
Yann Traonmilin, Jean-François Aujol
The sparse spike estimation problem consists in estimating a number of off-the-grid impulsive sources from under-determined linear measurements. Information theoretic results ensur…
Optimal convergence rates for Nesterov acceleration
Jean François Aujol, Charles Dossal, Aude Rondepierre
In this paper, we study the behavior of solutions of the ODE associated to Nesterov acceleration. It is well-known since the pioneering work of Nesterov that the rate of convergenc…
Variational Methods for Normal Integration
Yvain Quéau, Jean-Denis Durou, Jean-François Aujol
The need for an efficient method of integration of a dense normal field is inspired by several computer vision tasks, such as shape-from-shading, photometric stereo, deflectometry,…