Room 02.124, Building 5, St Priest campus Machine Learning in Montpellier, Theory & Practice Estimating matrices in the symmetric positive-definite (SPD) cone is of interest for many applications ranging from computer vision to graph learning. While there exist various convex optimization-based estimators, they remain limited in expressivity due to their model-based approach. The success of deep learning motivates the use of learning-based approaches to estimate SPD matrices with neural networks in a data-driven fashion. However, designing effective neural architectures for SPD learning is challenging, particularly when the task requires additional structural constraints, such as element-wise sparsity. Current approaches either do not ensure that the output meets all desired properties or lack expressivity. We introduce SpodNet, a novel and generic learning module that guarantees SPD outputs and supports additional structural constraints. Notably, it solves the challenging task of learning jointly SPD and sparse matrices. Machine Learning in Montpellier, Theory & Practice

JJ Moreau, campus Saint Priest de l’Université de Montpellier, bâtiment 2 Prof. Jean-Gabriel Ganascia In collaboration with the AI Transversal Axis of LIRMM and the EU AI4CCAM project, Professor Jean Gabriel Ganascia will give a lecture titled "The braided structure of time in AI and information technologies" at LIRMM. The lecture will be followed by light refreshments. Professor Ganascia is a renowned authority in Artificial Intelligence and Ethics. He is a Professor of Computer Science at the Faculty of Sciences of Sorbonne University and senior member of the Institut Universitaire de France. He is a EurAI Fellow – European Association for Artificial Intelligence and is currently the president of the Ethical Committee of CNRS. AI, Technologies, Computer Science at the Faculty