332 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- École Normale Supérieure Paris-SaclayFR60 papers
- École Normale Supérieure - PSLFR47 papers
- Centre National de la Recherche ScientifiqueFR38 papers
- Université Paris-SaclayFR31 papers
- CentraleSupélecFR9 papers
- Laboratoire Méthodes FormellesFR8 papers
- Centre BorelliFR7 papers
- Institut national de recherche en sciences et technologies du numériqueFR5 papers
- Laboratoire de Mécanique Paris-SaclayFR5 papers
- Laboratoire Interdisciplinaire des Sciences du NumériqueFR5 papers
- University of CambridgeGB5 papers
- Commissariat à l'Énergie Atomique et aux Énergies AlternativesFR4 papers
13 papers · 1 filter
Budgeted Online Influence Maximization
Pierre Perrault, Jennifer Healey, Zheng Wen +1
We introduce a new budgeted framework for online influence maximization, considering the total cost of an advertising campaign instead of the common cardinality constraint on a cho…
Deep Generative Methods and Tire Architecture Design
Fouad Oubari, Raphael Meunier, Rodrigue Décatoire +1
As deep generative models proliferate across the AI landscape, industrial practitioners still face critical yet unanswered questions about which deep generative models best suit co…
Fast 3D Diffusion for Scalable Granular Media Synthesis
Muhammad Moeeze Hassan, Régis Cottereau, Filippo Gatti +1
Discrete Element Method (DEM) simulations of granular media are computationally intensive, particularly during initialization phases dominated by large displacements and kinetic en…
Breaking the Black Box: Inherently Interpretable Physics-Constrained Machine Learning With Weighted Mixed-Effects for Imbalanced Seismic Data
Vemula Sreenath, Filippo Gatti, Pierre Jehel
Ground motion models (GMMs) are critical for seismic risk mitigation and infrastructure design. Machine learning (ML) is increasingly applied to GMM development due to expanding st…
Multi-Component VAE with Gaussian Markov Random Field
Fouad Oubari, Mohamed El-Baha, Raphael Meunier +2
Multi-component datasets with intricate dependencies, like industrial assemblies or multi-modal imaging, challenge current generative modeling techniques. Existing Multi-component…
Integrating Fourier Neural Operators with Diffusion Models to improve Spectral Representation of Synthetic Earthquake Ground Motion Response
Niccolò Perrone, Fanny Lehmann, Hugo Gabrielidis +2
Nuclear reactor buildings must be designed to withstand the dynamic load induced by strong ground motion earthquakes. For this reason, their structural behavior must be assessed in…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Leveraging Graph Neural Networks to Forecast Electricity Consumption
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +1
Accurate electricity demand forecasting is essential for several reasons, especially as the integration of renewable energy sources and the transition to a decentralized network pa…
Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamics
Fanny Lehmann, Filippo Gatti, Didier Clouteau
Numerical simulations are essential tools to evaluate the solution of the wave equation in complex settings, such as three-dimensional (3D) domains with heterogeneous properties. H…
Approximate Heavy Tails in Offline (Multi-Pass) Stochastic Gradient Descent
Krunoslav Lehman Pavasovic, Alain Durmus, Umut Simsekli
A recent line of empirical studies has demonstrated that SGD might exhibit a heavy-tailed behavior in practical settings, and the heaviness of the tails might correlate with the ov…
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
Ambroise Odonnat, Vasilii Feofanov, Ievgen Redko
Self-training is a well-known approach for semi-supervised learning. It consists of iteratively assigning pseudo-labels to unlabeled data for which the model is confident and treat…
Upper and Lower Bounds on the Performance of Kernel PCA
Maxime Haddouche, Benjamin Guedj, John Shawe-Taylor
Principal Component Analysis (PCA) is a popular method for dimension reduction and has attracted an unfailing interest for decades. More recently, kernel PCA (KPCA) has emerged as…
Tensor Convolutional Sparse Coding with Low-Rank activations, an application to EEG analysis
Pierre Humbert, Laurent Oudre, Nivolas Vayatis +1
Recently, there has been growing interest in the analysis of spectrograms of ElectroEncephaloGram (EEG), particularly to study the neural correlates of (un)-consciousness during Ge…