96 citations
- Université Paris-SaclayFR6 papers
- CentraleSupélecFR4 papers
- Mathématiques et Informatique pour la Complexité et les Systèmes4 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Département mathématiques, informatique, sciences de la donnée et technologies du numériqueFR2 papers
- Institut Polytechnique de ParisFR2 papers
- Laboratoire d'Ingénierie Circulation TransportsFR2 papers
- Laboratoire Interdisciplinaire des Sciences du NumériqueFR2 papers
- Sorbonne UniversitéFR2 papers
- Technical University of MunichDE2 papers
- Télécom SudParisFR2 papers
- Université de RennesFR2 papers
8 papers · 1 filter
Data Curation Matters: Model Collapse and Spurious Shift Performance Prediction from Training on Uncurated Text Embeddings
Lucas Mattioli, Youness Ait Hadichou, Sabrina Chaouche +1
Training models on uncurated Text Embeddings (TEs) derived from raw tabular data can lead to a severe failure mode known as model collapse, where predictions converge to a single c…
NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis
Mouadh Yagoubi, David Danan, Milad Leyli-Abadi +15
The integration of machine learning (ML) into the physical sciences is reshaping computational paradigms, offering the potential to accelerate demanding simulations such as computa…
A new methodology to decompose a parametric domain using reduced order data manifold in machine learning
Chetra Mang, Axel TahmasebiMoradi, Mouadh Yagoubi
We propose a new methodology for parametric domain decomposition using iterative principal component analysis. Starting with iterative principle component analysis, the high dimens…
Statistical and Predictive Analysis to Identify Risk Factors and Effects of Post COVID-19 Syndrome
Milad Leyli-abadi, Jean-Patrick Brunet, Axel Tahmasebimoradi
Based on recent studies, some COVID-19 symptoms can persist for months after infection, leading to what is termed long COVID. Factors such as vaccination timing, patient characteri…
ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)
Mouadh Yagoubi, Milad Leyli-Abadi, David Danan +6
The use of machine learning (ML) techniques to solve complex physical problems has been considered recently as a promising approach. However, the evaluation of such learned physica…
Data-driven Reachability using Christoffel Functions and Conformal Prediction
Abdelmouaiz Tebjou, Goran Frehse, Faïcel Chamroukhi
An important mathematical tool in the analysis of dynamical systems is the approximation of the reach set, i.e., the set of states reachable after a given time from a given initial…