8 citations · 27 across the 12 of their papers we have counts for
5 papers · 1 filter
One-step differentiation of iterative algorithms
Jérôme Bolte, Edouard Pauwels, Samuel Vaiter
In appropriate frameworks, automatic differentiation is transparent to the user at the cost of being a significant computational burden when the number of operations is large. For…
The derivatives of Sinkhorn-Knopp converge
Edouard Pauwels, Samuel Vaiter
We show that the derivatives of the Sinkhorn-Knopp algorithm, or iterative proportional fitting procedure, converge towards the derivatives of the entropic regularization of the op…
Supervised learning of analysis-sparsity priors with automatic differentiation
Hashem Ghanem, Joseph Salmon, Nicolas Keriven +1
Sparsity priors are commonly used in denoising and image reconstruction. For analysis-type priors, a dictionary defines a representation of signals that is likely to be sparse. In…
Low Complexity Regularization of Linear Inverse Problems
Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili
Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possi…
Risk estimation for matrix recovery with spectral regularization
Charles-Alban Deledalle, Samuel Vaiter, Gabriel Peyré +2
In this paper, we develop an approach to recursively estimate the quadratic risk for matrix recovery problems regularized with spectral functions. Toward this end, in the spirit of…