7 citations · 19 across the 8 of their papers we have counts for
8 papers
Decoupled Solution for Composite Sparse-plus-Smooth Inverse Problems
Adrian Jarret, Julien Fageot
We consider composite linear inverse problems where the signal to recover is modeled as a sum of two functions. We study a variational framework formulated as an optimization probl…
PolyCLEAN: Atomic Optimization for Super-Resolution Imaging and Uncertainty Estimation in Radio Interferometry
Adrian Jarret, Sepand Kashani, Joan Rué-Queralt +3
Aims: We address two issues for the adoption of convex optimization in radio interferometric imaging. First, a method for a fine resolution setup is proposed which scales naturally…
A Decoupled Approach for Composite Sparse-plus-Smooth Penalized Optimization
Adrian Jarret, Valérie Costa, Julien Fageot
We consider a linear inverse problem whose solution is expressed as a sum of two components: one smooth and the other sparse. This problem is addressed by minimizing an objective f…
HVOX: Scalable Interferometric Synthesis and Analysis of Spherical Sky Maps
Sepand Kashani, Joan Rué Queralt, Adrian Jarret +1
Analysis and synthesis are key steps of the radio-interferometric imaging process, serving as a bridge between visibility and sky domains. They can be expressed as partial Fourier…
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSO
Adrian Jarret, Julien Fageot, Matthieu Simeoni
We propose a fast and scalable Polyatomic Frank-Wolfe (P-FW) algorithm for the resolution of high-dimensional LASSO regression problems. The latter improves upon traditional Frank-…
Spline-based Reconstruction of Periodic Signals with Sparse Innovations
Adrian Jarret
Optimization-based problems have become of great interest for signal approximation purposes, as they achieved good accuracy results while being extremely flexible and versatile. In…
About Graph Degeneracy, Representation Learning and Scalability
Simon Brandeis, Adrian Jarret, Pierre Sevestre
Graphs or networks are a very convenient way to represent data with lots of interaction. Recently, Machine Learning on Graph data has gained a lot of traction. In particular, verte…
Desynchronization of Random Dynamical System under Perturbation by an Intrinsic Noise
Adrian Jarret
In the theory of random dynamical systems (RDS), individuals with different initial states follow a same law of motion that is stochastically changing with time | called extrinsic…