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X-WR-CALNAME:GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
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X-WR-CALDESC:évènements pour GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
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DTSTART:20250330T010000
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DTSTART;TZID=Europe/Paris:20250403T140000
DTEND;TZID=Europe/Paris:20250403T150000
DTSTAMP:20260419T110059
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SUMMARY:Séminaire Image : "Decomposable Symbolic Regression Using Transformers and Neural Network-Assisted Genetic Algorithms"\,  Giorgio Morales
DESCRIPTION:Nous aurons le plaisir d’écouter Giorgio Morales\, post-doc de l’équipe Image.\nIl donnera un séminaire IMAGE le jeudi 3 avril 2025 à 14h en salle de séminaire F-200. \nTitre : « Decomposable Symbolic Regression Using Transformers and Neural Network-Assisted Genetic Algorithms » \nRésumé :\nOne of the goals of science is to discover laws that serve as causal explanations for the observable world. Such discoveries may stem from distilling experimental data into analytical equations that allow interpretation of their underlying natural laws. This process is known as equation learning or symbolic regression (SR). Nevertheless\, most SR methods prioritize minimizing prediction error over identifying the governing equations\, often producing overly complex or inaccurate expressions. To address this\, in this talk\, I present a decomposable SR method that generates interpretable multivariate expressions leveraging transformer models\, genetic algorithms (GAs)\, and genetic programming (GP). It first generates multiple univariate skeletons that capture the functional relationship between each variable and the system’s response. These skeletons are systematically merged using evolutionary approaches\, ensuring interpretability throughout the process. The method was evaluated on problems with controlled and varying degrees of noise\, demonstrating lower or comparable interpolation and extrapolation errors compared to two GP-based and two neural SR methods. Unlike these methods\, this approach consistently learned expressions that matched the original mathematical structure.
URL:https://www.greyc.fr/event/seminaire-image-decomposable-symbolic-regression-using-transformers-and-neural-network-assisted-genetic-algorithms-giorgio-morales/
LOCATION:ENSICAEN – Batiment F – Salle F-200\, 6 Bd Maréchal Juin\, Caen\, 14050\, France
CATEGORIES:Image,Seminaire Image
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DTSTART;TZID=Europe/Paris:20250424T140000
DTEND;TZID=Europe/Paris:20250424T150000
DTSTAMP:20260419T110059
CREATED:20250326T101124Z
LAST-MODIFIED:20250326T101124Z
UID:11819-1745503200-1745506800@www.greyc.fr
SUMMARY:Séminaire Image : "Multi-modal Identity Extraction"\, Ryan Webster
DESCRIPTION:Nous aurons le plaisir d’écouter Ryan Webster\, post-doctorant à l’INRIA Rennes.\nIl donnera un séminaire IMAGE le jeudi 24 avril 2025 à 14h en salle de séminaire F-200. \nTitre : « Multi-modal Identity Extraction » \nRésumé : \nFoundational Vision Language Models (VLMs) exhibit extremely general capabilities derived from their web-scale training data. Because web data inherently contains visual and textual information about people\, using VLMs to identify people has been raised as a potential issue. Recent work demonstrates that an attacker can infer whether a VLM was trained on a person’s images\, given they possess the ground truth face/name pair. In this work\, we show an attacker can extract names of individuals given images only\, which we dub Identity Extraction. We first derive a baseline black-box extraction attack by building off of previous research on membership inference. In the most difficult setting\, where no ground truth names are available to the attacker\, we demonstrate successful extractions via reinforcement learning. As we scaled up the evaluation set from previous work\, we finally show the interest of our work as a model auditing tool. \n 
URL:https://www.greyc.fr/event/seminaire-image-multi-modal-identity-extraction-ryan-webster/
LOCATION:ENSICAEN – Batiment F – Salle F-200\, 6 Bd Maréchal Juin\, Caen\, 14050\, France
CATEGORIES:Image,Seminaire Image
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