19 citations · 37 across the 18 of their papers we have counts for
8 papers · 1 filter
Solution of Mismatched Monotone+Lipschitz Inclusion Problems
Emilie Chouzenoux, Jean-Christophe Pesquet, Fernando Roldán
In this article, we study the convergence of algorithms for solving monotone inclusions in the presence of adjoint mismatch. The adjoint mismatch arises when the adjoint of a linea…
Aggregated f-average Neural Network applied to Few-Shot Class Incremental Learning
Mathieu Vu, Emilie Chouzenoux, Ismail Ben Ayed +1
Ensemble learning leverages multiple models (i.e., weak learners) on a common machine learning task to enhance prediction performance. Basic ensembling approaches average the weak…
Majorization-Minimization for sparse SVMs
Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini +5
Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework. Nowadays, they often outperform other…
A new non-convex framework to improve asymptotical knowledge on generic stochastic gradient descent
Jean-Baptiste Fest, Audrey Repetti, Emilie Chouzenoux
Stochastic gradient optimization methods are broadly used to minimize non-convex smooth objective functions, for instance when training deep neural networks. However, theoretical g…
Sparse Graphical Linear Dynamical Systems
Emilie Chouzenoux, Victor Elvira
Time-series datasets are central in machine learning with applications in numerous fields of science and engineering, such as biomedicine, Earth observation, and network analysis.…
GraphIT: Iterative reweighted algorithm for sparse graph inference in state-space models
Emilie Chouzenoux, Victor Elvira
State-space models (SSMs) are a common tool for modeling multi-variate discrete-time signals. The linear-Gaussian (LG) SSM is widely applied as it allows for a closed-form solution…