2 citations · 3 across the 6 of their papers we have counts for
6 papers
Bayesian ECG reconstruction using denoising diffusion generative models
Gabriel V. Cardoso, Lisa Bedin, Josselin Duchateau +2
In this work, we propose a denoising diffusion generative model (DDGM) trained with healthy electrocardiogram (ECG) data that focuses on ECG morphology and inter-lead dependence. O…
Monte Carlo guided Diffusion for Bayesian linear inverse problems
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff +1
Ill-posed linear inverse problems arise frequently in various applications, from computational photography to medical imaging. A recent line of research exploits Bayesian inference…
State and parameter learning with PaRIS particle Gibbs
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff +2
Non-linear state-space models, also known as general hidden Markov models, are ubiquitous in statistical machine learning, being the most classical generative models for serial dat…
Particle-based, rapid incremental smoother meets particle Gibbs
Gabriel Cardoso, Eric Moulines, Jimmy Olsson
The particle-based, rapid incremental smoother (PARIS) is a sequential Monte Carlo technique allowing for efficient online approximation of expectations of additive functionals und…
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling
Gabriel Cardoso, Sergey Samsonov, Achille Thin +2
Importance Sampling (IS) is a method for approximating expectations under a target distribution using independent samples from a proposal distribution and the associated importance…
Generative methods for sampling transition paths in molecular dynamics
Tony Lelièvre, Geneviève Robin, Inass Sekkat +2
Molecular systems often remain trapped for long times around some local minimum of the potential energy function, before switching to another one -- a behavior known as metastabili…