Clément Chadebec

Orcid: 0000-0003-3890-1392

According to our database1, Clément Chadebec authored at least 14 papers between 2020 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2025
MultiVae: A Python package for Multimodal Variational Autoencoders on Partial Datasets.
J. Open Source Softw., June, 2025

LBM: Latent Bridge Matching for Fast Image-to-Image Translation.
CoRR, March, 2025

Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation.
Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024
Controllable Shadow Generation with Single-Step Diffusion Models from Synthetic Data.
CoRR, 2024

2023
Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder.
IEEE Trans. Pattern Anal. Mach. Intell., March, 2023

Modeling the latent space of variational autoencoders. (Modélisation et structuration de l'espace latent des auto-encodeurs variationnels).
PhD thesis, 2023

Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis.
CoRR, 2023

Variational Inference for Longitudinal Data Using Normalizing Flows.
CoRR, 2023

2022
Pythae: Unifying Generative Autoencoders in Python - A Benchmarking Use Case.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

A Geometric Perspective on Variational Autoencoders.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

An Image Feature Mapping Model for Continuous Longitudinal Data Completion and Generation of Synthetic Patient Trajectories.
Proceedings of the Deep Generative Models - Second MICCAI Workshop, 2022

2021
Data Generation in Low Sample Size Setting Using Manifold Sampling and a Geometry-Aware VAE.
CoRR, 2021

Data Augmentation with Variational Autoencoders and Manifold Sampling.
Proceedings of the Deep Generative Models, and Data Augmentation, Labelling, and Imperfections, 2021

2020
Geometry-Aware Hamiltonian Variational Auto-Encoder.
CoRR, 2020


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