247 citations · 289 across the 7 of their papers we have counts for
7 papers
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
Kais Hariz, Hachem Kadri, Stéphane Ayache +2
We study the implicit regularization effects of deep learning in tensor factorization. While implicit regularization in deep matrix and 'shallow' tensor factorization via linear an…
Geometries on the cone of positive-definite matrices derived from the power potential and their relation to the power means
Nadia Chouaieb, Bruno Iannazzo, Maher Moakher
We study a Riemannian metric on the cone of symmetric positive-definite matrices obtained from the Hessian of the power potential function . We give explicit expre…
A nonstandard higher-order variational model to speckle noise removal and thin-structure detection
Hamdi Houichet, Anis Theljani, Maher Moakher +1
In this work, we propose a multiscale approach for a nonstandard higher-order PDE based on the -Kirchhoff energy. First, we consider a topological gradient approach for a…
Diagonality Measures of Hermitian Positive-Definite Matrices with Application to the Approximate Joint Diagonalization Problem
Khaled Alyani, Marco Congedo, Maher Moakher
In this paper, we introduce properly-invariant diagonality measures of Hermitian positive-definite matrices. These diagonality measures are defined as distances or divergences betw…
Approximate Joint Diagonalization and Geometric Mean of Symmetric Positive Definite Matrices
Marco Congedo, Bijan Afsari, Alexandre Barachant +1
We explore the connection between two problems that have arisen independently in the signal processing and related fields: the estimation of the geometric mean of a set of symmetri…
Riemannian level-set methods for tensor-valued data
Mourad Zerai, Maher Moakher
We present a novel approach for the derivation of PDE modeling curvature-driven flows for matrix-valued data. This approach is based on the Riemannian geometry of the manifold of S…