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20052026
most citedHow to estimate carbon footprint when training deep learning models? A guide and review

120 citations

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5 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

Robust Barycenters of Persistence Diagrams

Keanu Sisouk, Eloi Tanguy, Julie Delon +1

This short paper presents a general approach for computing robust Wasserstein barycenters of persistence diagrams. The classical method consists in computing assignment arithmetic…

cs.LG2023★ 2 cited

Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses

Eloi Tanguy

Optimal Transport has sparked vivid interest in recent years, in particular thanks to the Wasserstein distance, which provides a geometrically sensible and intuitive way of compari…

cs.LG2023★ 120 cited

How to estimate carbon footprint when training deep learning models? A guide and review

Lucia Bouza Heguerte, Aurélie Bugeau, Loïc Lannelongue

Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowled…

cs.LG2012★ 40 cited

Craniofacial reconstruction as a prediction problem using a Latent Root Regression model

Maxime Berar, Françoise Tilotta, Joan Alexis Glaunès +1

In this paper, we present a computer-assisted method for facial reconstruction. This method provides an estimation of the facial shape associated with unidentified skeletal remains…