Search Results for author: Stéphanie Allassonnière

Found 16 papers, 6 papers with code

T-Rep: Representation Learning for Time Series using Time-Embeddings

no code implementations6 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.

Anomaly Detection Representation Learning +1

Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis

no code implementations19 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.

Variational Inference for Longitudinal Data Using Normalizing Flows

1 code implementation24 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.

Imputation Variational Inference

A Geometric Perspective on Variational Autoencoders

no code implementations15 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.

Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case

1 code implementation16 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.

Benchmarking Density Estimation +2

Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder

2 code implementations30 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.

Data Augmentation Specificity

Data Augmentation with Variational Autoencoders and Manifold Sampling

1 code implementation25 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.

Data Augmentation

Geometry-Aware Hamiltonian Variational Auto-Encoder

1 code implementation22 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.

Clustering Dimensionality Reduction

Mixture of Conditional Gaussian Graphical Models for unlabelled heterogeneous populations in the presence of co-factors

1 code implementation19 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.

Deterministic Approximate EM Algorithm; Application to the Riemann Approximation EM and the Tempered EM

no code implementations23 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.

Gaussian Graphical Model exploration and selection in high dimension low sample size setting

no code implementations11 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.

Learning from both experts and data

no code implementations20 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.

Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects

no code implementations5 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.

Data Augmentation

Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks

no code implementations25 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.

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