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20112025
most citedGlobal Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery

97 citations

Showing cs.LGShow all

17 papers · 1 filter

cs.LG2025

Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transport

Samuel Boïté, Eloi Tanguy, Julie Delon +2

The Expectation-Maximisation (EM) algorithm is a central tool in statistics and machine learning, widely used for latent-variable models such as Gaussian Mixture Models (GMMs). Des…

cs.LG2025

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…

cs.LG2025

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…

cs.LG2025

Spiking Neural Models for Decision-Making Tasks with Learning

Sophie Jaffard, Giulia Mezzadri, Patricia Reynaud-Bouret +1

In cognition, response times and choices in decision-making tasks are commonly modeled using Drift Diffusion Models (DDMs), which describe the accumulation of evidence for a decisi…

cs.LG2024★ 2 cited

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…

cs.LG2024★ 10 cited

Geometry-aware framework for deep energy method: an application to structural mechanics with hyperelastic materials

Thi Nguyen Khoa Nguyen, Thibault Dairay, Raphaël Meunier +2

Physics-Informed Neural Networks (PINNs) have gained considerable interest in diverse engineering domains thanks to their capacity to integrate physical laws into deep learning mod…