Olivier Bernard
Professor at the university of Lyon (INSA), France and deputy director of the CREATIS laboratory
- Lyon, France
- Github
- Google Scholar
- ORCID
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Introduction to Bayesian inference
less than 10 minute read
Published:
This post is intended for readers who want to understand the theoretical concepts behind Bayesian inference and its applications in deep learning, including variational inference (e.g., VAEs) and amortized simulation-based inference (e.g., tabPFN).
The denoising diffusion probabilistic models (DDPM) paradigm demystified
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Published:
This post is aimed at those who want to understand the mathematical framework of denoising diffusion probabilistic model and its implementation in deep learning.
What about the conditional variational autoencoder?
less than 10 minute read
Published:
In this post I explain the mathematics behind conditional variational autoencoders and the differences with conventional variational autoencoders.
The beauty of contrastive learning: a new step toward efficient unsupervised learning
less than 10 minute read
Published:
The aim of this post is to present the key stages/concepts of contrastive learning.