3 citations · 11 across the 18 of their papers we have counts for
7 papers · 1 filter
Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
Badr Moufad, Albina Ilina, Hai Victor Habi +4
Commercial Microwave Links (CMLs) offer dense spatial coverage for rainfall sensing but produce path-integrated measurements that make accurate ground-level reconstruction challeng…
Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation
Samson Gourevitch, Yazid Janati, Dario Shariatian +4
Discrete diffusion models are often trained through clean-data prediction, but the prediction can be used in different ways to define the reverse dynamics. In Masked Diffusion Mode…
When Test-Time Guidance Is Enough: Fast Image and Video Editing with Diffusion Guidance
Ahmed Ghorbel, Badr Moufad, Navid Bagheri Shouraki +5
Text-driven image and video editing can be naturally cast as inpainting problems, where masked regions are reconstructed to remain consistent with both the observed content and the…
Sparse Scheduled Diffusion Guidance for Inverse Problems
Abduragim Shtanchaev, Albina Ilina, Yazid Janati +3
Pretrained diffusion models are effective priors for Bayesian inverse problems, but posterior sampling with these priors is often costly because data-consistency guidance is applie…
Categorical Reparameterization with Denoising Diffusion models
Samson Gourevitch, Alain Durmus, Eric Moulines +2
Learning models with categorical variables requires optimizing expectations over discrete distributions, a setting in which stochastic gradient-based optimization is challenging du…
Fast and Robust Likelihood-Guided Diffusion Posterior Sampling with Amortized Variational Inference
Léon Zheng, Thomas Hirtz, Yazid Janati +1
Zero-shot diffusion posterior sampling offers a flexible framework for inverse problems by accommodating arbitrary degradation operators at test time, but incurs high computational…
Entropic Mirror Monte Carlo
Anas Cherradi, Yazid Janati, Alain Durmus +3
Importance sampling is a Monte Carlo method which designs estimators of expectations under a target distribution using weighted samples from a proposal distribution. When the targe…