Séminaire Performance et Généricité
À propos du séminaire
La modélisation orientée objet permet la classification des problèmes de calcul scientifique, et par conséquent, par la factorisation qu'elle rend possible, elle fournit un excellent support pour la fédération d'efforts de développement. Malheureusement les performances en pâtissent souvent. De nouveaux langages, de nouvelles techniques de programmation réconcilient performance et généricité, permettant la naissance de bibliothèques de nouvelle génération (Boost, Olena, Vcsn, etc.).
L'objet de ce séminaire est la diffusion du savoir et des compétences sur la modélisation de bibliothèques métiers génériques et performantes.
Mots clés: Calcul Scientifique, Distribution, Génie Logiciel, Généricité, Grille, Langages, Multi-cœur, Paradigmes de Programmation, Parallélisme, Recherche reproductible.
Comment venir: Contact.
Scaling Optimal Transport for High Dimensional Learning
Gabriel Peyré, CNRS and Ecole Normale Supérieure
Optimal transport (OT) has recently gained a lot of interest in machine learning. It is a natural tool to compare in a geometrically faithful way probability distributions. It finds applications in both supervised learning (using geometric loss functions) and unsupervised learning (to perform generative model fitting). OT is however plagued by the curse of dimensionality, since it might require a number of samples which grows exponentially with the dimension. In this talk, I will review entropic regularization methods which define geometric loss functions approximating OT with a better sample complexity.
Gabriel Peyré is a CNRS senior researcher and professor at Ecole Normale Supérieure, Paris. He works at the interface between applied mathematics, imaging and machine learning. He obtained 2 ERC grants (Starting in 2010 and Consolidator in 2017), the Blaise Pascal prize from the French academy of sciences in 2017, the Magenes Prize from the Italian Mathematical Union in 2019 and the silver medal from CNRS in 2021. He is invited speaker at the European Congress for Mathematics in 2020. He is the deputy director of the Prairie Institute for artificial intelligence, the director of the ENS center for data science and the former director of the GdR CNRS MIA. He is the head of the ELLIS (European Lab for Learning & Intelligent Systems) Paris Unit. He is engaged in reproducible research and code education.
https://optimaltransport.github.io/, http://www.numerical-tours.com/, https://ellis-paris.github.io/
An Introduction to Topological Data Analysis with the Topology ToolKit
Julien Tierny, Sorbonne Université
Topological Data Analysis (TDA) is a recent area of computer science that focuses on discovering intrinsic structures hidden in data. Based on solid mathematical tools such as Morse theory and Persistent Homology, TDA enables the robust extraction of the main features of a data set into stable, concise, and multi-scale descriptors that facilitate data analysis and visualization. In this talk, I will give an intuitive overview of the main tools used in TDA (persistence diagrams, Reeb graphs, Morse-Smale complexes, etc.) with applications to concrete use cases in computational fluid dynamics, medical imaging, quantum chemistry, and climate modeling. This talk will be illustrated with results produced with the "Topology ToolKit" (TTK), an open-source library (BSD license) that we develop with collaborators to showcase our research. Tutorials for re-producing these experiments are available on the TTK website.
Julien Tierny received his Ph.D. degree in Computer Science from the University of Lille in 2008 and
the Habilitation degree (HDR) from Sorbonne University in 2016. Currently a CNRS permanent
research scientist affiliated with Sorbonne University, his research expertise lies in topological methods
for data analysis and visualization. Author on the topic and award winner for his research, he regularly
serves as an international program committee member for the top venues in data visualization (IEEE VIS,
EuroVis, etc.) and is an associate editor for IEEE Transactions on Visualization and Computer Graphics.
Julien Tierny is also founder and lead developer of the Topology ToolKit (TTK), an open source library for
topological data analysis.
Contributions to Boolean satisfiability solving and its application to the analysis of discrete systems
Souheib Baarir, Université Paris VI
Despite its NP-completeness, propositional Boolean satisfiability (SAT) covers a broad spectrum of applications. Nowadays, it is an active research area finding its applications in many contexts like planning decision, cryptology, computational biology, hardware and software analysis. Hence, the development of approaches allowing to handle increasingly challenging SAT problems has become a major focus: during the past eight years, SAT solving has been the main subject of my research work. This talk presents some of the main results we obtained in the field.
Souheib Baarir est Docteur en informatique de l'Université de Paris VI depuis 2007 et a obtenu son HDR à Sorbonne Université en 2019. Le thème de ses recherches s'inscrit dans le cadre des méthodes formelles de vérification des systèmes concurrents. En particulier, il s’intéresse aux méthodes permettant d’optimiser la vérification en exploitant le parallélisme et/ou les propriétés de symétries apparaissant dans de tels systèmes.
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