no code implementations • 6 Oct 2023 • Archibald Fraikin, Adrien Bennetot, Stéphanie Allassonnière
Multivariate time series present challenges to standard machine learning techniques, as they are often unlabeled, high dimensional, noisy, and contain missing data.
no code implementations • 19 May 2023 • Agathe Senellart, Clément Chadebec, Stéphanie Allassonnière
We propose a new multimodal variational autoencoder that enables to generate from the joint distribution and conditionally to any number of complex modalities.
1 code implementation • 24 Mar 2023 • Clément Chadebec, Stéphanie Allassonnière
This paper introduces a new latent variable generative model able to handle high dimensional longitudinal data and relying on variational inference.
no code implementations • 15 Sep 2022 • Clément Chadebec, Stéphanie Allassonnière
This paper introduces a new interpretation of the Variational Autoencoder framework by taking a fully geometric point of view.
1 code implementation • 16 Jun 2022 • Clément Chadebec, Louis J. Vincent, Stéphanie Allassonnière
In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions.
2 code implementations • 30 Apr 2021 • Clément Chadebec, Elina Thibeau-Sutre, Ninon Burgos, Stéphanie Allassonnière
In this paper, we propose a new method to perform data augmentation in a reliable way in the High Dimensional Low Sample Size (HDLSS) setting using a geometry-based variational autoencoder.
1 code implementation • 25 Mar 2021 • Clément Chadebec, Stéphanie Allassonnière
We propose a new efficient way to sample from a Variational Autoencoder in the challenging low sample size setting.
no code implementations • 27 Oct 2020 • Frédéric Logé, Rémi Besson, Stéphanie Allassonnière
A patient suffering from a rare disease in France has to wait an average of two years before being diagnosed.
1 code implementation • 22 Oct 2020 • Clément Chadebec, Clément Mantoux, Stéphanie Allassonnière
Their ability to capture meaningful information from the data can be easily apprehended when considering their capability to generate new realistic samples or perform potentially meaningful interpolations in a much smaller space.
1 code implementation • 19 Jun 2020 • Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière
We demonstrate on synthetic and real data how this method fulfils its goal and succeeds in identifying the sub-populations where the Mixtures of GGM are disrupted by the effect of the co-features.
no code implementations • 23 Mar 2020 • Thomas Lartigue, Stanley Durrleman, Stéphanie Allassonnière
In this paper, we introduce a theoretical framework, with state-of-the-art convergence guarantees, for any deterministic approximation of the E step.
no code implementations • 11 Mar 2020 • Thomas Lartigue, Simona Bottani, Stephanie Baron, Olivier Colliot, Stanley Durrleman, Stéphanie Allassonnière
We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one.
no code implementations • 20 Oct 2019 • Rémi Besson, Erwan Le Pennec, Stéphanie Allassonnière
In this context, it is common to rely first on an initial domain knowledge a priori before proceeding to an online data acquisition.
no code implementations • 5 Apr 2019 • Igor Koval, Stéphanie Allassonnière, Stanley Durrleman
The ability to predict the progression of biomarkers, notably in NDD, is limited by the size of the longitudinal data sets, in terms of number of patients, number of visits per patients and total follow-up time.
no code implementations • 25 Nov 2018 • Rémi Besson, Erwan Le Pennec, Stéphanie Allassonnière, Julien Stirnemann, Emmanuel Spaggiari, Antoine Neuraz
In this work, we present our various contributions to the objective of building a decision support tool for the diagnosis of rare diseases.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 25 Sep 2017 • Igor Koval, Jean-Baptiste Schiratti, Alexandre Routier, Michael Bacci, Olivier Colliot, Stéphanie Allassonnière, Stanley Durrleman
Model parameters show the variability of this average pattern of atrophy in terms of trajectories across brain regions, age at disease onset and pace of propagation.