1 code implementation • 18 Mar 2024 • Mariam Hassan, Florent Forest, Olga Fink, Malcolm Mielle
Thermal scene reconstruction exhibit great potential for ap- plications across a broad spectrum of fields, including building energy consumption analysis and non-destructive testing.
no code implementations • 24 Jan 2024 • Ismail Nejjar, Gaetan Frusque, Florent Forest, Olga Fink
Our approach serves a dual purpose: providing a measure of confidence in predictions and acting as a regularization of the embedding space.
no code implementations • 5 Dec 2023 • Florent Forest, Olga Fink
However, deep learning models usually only perform well on the data distribution they have been trained on.
no code implementations • 20 Sep 2023 • Florent Forest, Hugo Porta, Devis Tuia, Olga Fink
This paper proposes applying this methodology to segment and monitor surface cracks.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
no code implementations • 5 Oct 2022 • Abdellah Madane, Mohamed-djallel Dilmi, Florent Forest, Hanane Azzag, Mustapha Lebbah, Jerome Lacaille
One of its limitations is that it may generate a random multivariate time series; it may fail to generate samples in the presence of multiple sub-components within an overall distribution.
1 code implementation • 11 Nov 2020 • Florent Forest, Mustapha Lebbah, Hanane Azzag, Jérôme Lacaille
Quantitative evaluation of self-organizing maps (SOM) is a subset of clustering validation, which is a challenging problem as such.
1 code implementation • 15 Jun 2020 • Alex Mourer, Florent Forest, Mustapha Lebbah, Hanane Azzag, Jérôme Lacaille
In this perspective, clustering stability has emerged as a natural and model-agnostic principle: an algorithm should find stable structures in the data.
1 code implementation • ESANN 2019 2019 • Florent Forest, Mustapha Lebbah, Hanene Azzag, Jérôme Lacaille
In the wake of recent advances in joint clustering and deep learning, we introduce the Deep Embedded Self-Organizing Map, a model that jointly learns representations and the code vectors of a self-organizing map.