Publications associées à Orange (logiciel)

Failure and success of the spectral bias prediction for Laplace Kernel Ridge Regression: the case of low-dimensional data

Matthieu Wyart, Umberto Maria Tomasini, Antonio Sclocchi

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a 'spectral bias': decomposing the true function f* on the eigenbasis of the ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2022

Simple automation of SEM-EDS spectral maps analysis with Python and the edxia framework

Karen Scrivener, Fabien Jacques Michel Georget, William Wilson

In a recent article, we described the edxia framework, a user-friendly framework to analyse the microstructure of cementitious materials using SEM-EDS hypermaps. The manual approach presented was shown to be efficient to answer the relevant scientific ques ...

WILEY2022

What is Trending on Wikipedia? Capturing Trends and Language Biases Across Wikipedia Editions

Pierre Vandergheynst, Nicolas Aspert, Volodymyr Miz, Benjamin Ricaud, Joëlle Hanna

In this work, we propose an automatic evaluation and comparison of the browsing behavior of Wikipedia readers that can be applied to any language editions of Wikipedia. As an example, we focus on English, French, and Russian languages during the last four ...

ACM2020

pyGenomeTracks: reproducible plots for multivariate genomic datasets

Lucille Delisle

Motivation Generating publication ready plots to display multiple genomic tracks can pose a serious challenge. Making desirable and accurate figures requires considerable effort. This is usually done by hand or using a vector graphic software. ...

2020

Numerical modeling and neural networks to identify constitutive parameters from in situ tests

Rafal Filip Obrzud

This study proposes a new advanced algorithm for determining material parameters based on in situ tests. In situ testing gives an opportunity to perform soil characterization in natural stress conditions on a representative soil mass. Most field techniques ...

EPFL2009

Counting pedestrians in video sequences using trajectory clustering

Jean-Philippe Thiran, Gianluca Antonini

In this paper we propose to use clustering methods for automatic counting of pedestrians in video sequences. As input, we consider the output of those detection/ tracking systems that overestimate the number of targets. Clustering techniques are applied to ...

2006