103 citations · 187 across the 20 of their papers we have counts for
23 papers
T-Rep: Representation Learning for Time Series using Time-Embeddings
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. To address this…
Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis
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. The unimodal posteri…
Variational Inference for Longitudinal Data Using Normalizing Flows
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. The time dependency between the…
A Geometric Perspective on Variational Autoencoders
Clément Chadebec, Stéphanie Allassonnière
This paper introduces a new interpretation of the Variational Autoencoder framework by taking a fully geometric point of view. We argue that vanilla VAE models unveil naturally a R…
Robust Reinforcement Learning with Distributional Risk-averse formulation
Pierre Clavier, Stéphanie Allassonière, Erwan Le Pennec
Robust Reinforcement Learning tries to make predictions more robust to changes in the dynamics or rewards of the system. This problem is particularly important when the dynamics an…
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Case
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. Among those models, variational autoencoders have g…