Catégorie : Séminaires projet SiMul

Séminaire Konstantin Usevich

10 juin 2024 @ 14h00 – 15h00 – Speaker: Konstantin Usevich (CRAN) Title: Algebraic Algorithms for the ParaTuck-2 Decomposition Abstract: ParaTuck-2 decomposition (PT2D) of 3-rd order tensors is a 2-level extension of the well-known CP (canonical polyadic) decomposition (CPD). It is relevant in several applications, such as chemometrics, telecommunications, and machine learning. As shown in (Harshman, Lundy, 1996), the PT2D enjoys strong uniqueness […]

Séminaire Ivan Yakushev – Application of machine learning in visual art

19 juin 2024 @ 9h30 – 12h30 – SiMul Seminar Program 9:30 – 10:30 Short Scientific Presentations: • Emilio Norberto Abata (M2): « Adaptation de domaine pour la prédiction de durée de vie de batteries industrielles » • Antoine Kickens (M2): « Réseaux de neurones lipschitziens » • Joppe De Jonghe (PhD): « Tensor-based methods for training multi-layer flexible neural networks » 10:30 – 11:30 Invited Talk: • Ivan […]

Séminaire Helena Calatrava (Northeastern University, Boston)

16 juillet 2024 @ 11h00 – 12h30 – Speaker: Helena Calatrava (Northeastern University, Boston, USA) Title: GNSS Signal Processing for Precise and Robust Positioning Abstract: In this talk, we will explore two methodologies designed to enhance the performance of Global Navigation Satellite Systems (GNSS): collaborative positioning techniques to improve positioning accuracy and robust signal processing to enhance resilience against jamming attacks. First, we […]

Séminaire Pardis Semnani (University of British Columbia, Vancouver)

22 juillet 2024 @ 14h00 – 15h00 – Speaker: Pardis Semnani (University of British Columbia, Vancouver, Canada) Website: https://sites.google.com/view/pardissemnani/home Date: July, 22 2024, 14h00-15h00 Title: Homaloidal polynomials and Gaussian models of maximum likelihood degree one Abstract: We study the Gaussian statistical models whose log-likelihood function has a unique complex critical point, i.e., has maximum likelihood degree one. We exploit the connection developed by […]

Séminaire de Lucas De Lara

14 octobre 2024 @ 13h00 – 14h00 – Speaker: Lucas de Lara, postdoctoral researcher at IECL/CRAN Title: On the nonconvexity of push-forward constraints and its consequences in machine learning Location: CRAN FST, bâtiment Henri Poincaré, 4ème étage Abstract: The push-forward operation enables one to redistribute a probability measure through a deterministic map. It plays a key role in statistics and optimization: many learning […]