Search Results for author:
Found 7 papers, 5 papers with code
Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis
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
This paper introduces a new latent variable generative model able to handle high dimensional longitudinal data and relying on variational inference.
A Geometric Perspective on Variational Autoencoders
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
In recent years, deep generative models have attracted increasing interest due to their capacity to model complex distributions.
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder
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 with Variational Autoencoders and Manifold Sampling
We propose a new efficient way to sample from a Variational Autoencoder in the challenging low sample size setting.
Geometry-Aware Hamiltonian Variational Auto-Encoder
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.
