Clément Chadebec

Research Scientist at Stability AI

I am a research scientist at Stability AI working on Deep Generative Models. I have a Ph.D in applied mathematics and machine learning focusing on generative models at INRIA. In particular, I worked on Variational Autoencoders (VAE) and was mostly interested in providing interesting and relevant modellings of their latent space. I also maintain Python libraries trying to democratize those models. Find more on github.

news

Oct 4, 2023 I am joining Stability AI as a research scientist. Let’s push the capabilities of generative models 🚀
Jun 29, 2023 Very happy to annouce that I have successfully defended my PhD thesis! 🥳 Thank you to all the members of the jury and Stéphanie for this amazingly enriching journey! ⛵
Mar 1, 2023 Thank you G-research for inviting me to present some advances in Variational Autoencoders and how to train them using Pythae! (Slides)
Nov 3, 2022 Thank you IBM Journal Club for inviting me to present my latest NeurIPS papers bringing a geometric perspective on VAEs! (Slides)
Sep 16, 2022 Our paper “Pythae: Unifying Generative Autoencoders in Python – A Benchmarking Use Case” is accepted to NeurIPS 2022!
Sep 14, 2022 Our paper “A Geometric Perspective on Variational Autoencoders” is accepted to NeurIPS 2022!
Jul 13, 2022 Our paper “An Image Feature Mapping Model for Continuous Longitudinal Data Completion and Generation of Synthetic Patient Trajectories” is accepted to DGM4MICCAI Workshop.
Jul 1, 2022 Our paper “Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder” is accepted to IEEE TPAMI!
Jun 17, 2022 After 8 months of long coding nights ☕️ we finally officially release Pythae 🥳, a python library unifying generative autoencoder implementations including vaegan 🥗, vqvae or RAEs. Check out our github repo.
Nov 15, 2021 Thank you so much MIA for inviting me today to present our work on geometry-based VAEs and data augmentation. Thanks again for these great and inspiring discussions!