1 citations · 1 across the 8 of their papers we have counts for
11 papers
Iterating Consistency Models: Stability, Error Bounds and Noise Schedules
Alessio Spagnoletti, Abdul-Lateef Haji-Ali, Andrés Almansa +3
Consistency models (CMs) have become a leading approach for generating high-quality samples in few steps. However, adding steps can improve or degrade sample quality in ways that a…
Perturb-and-Solve: Efficient Learned-Operator Conditioning for Latent Diffusion Inverse Problems
Abduragim Shtanchaev, Arip Asadulaev, Luiza Labazanova +3
Latent diffusion models serve as powerful priors for solving inverse problems in image restoration, such as deblurring, inpainting, and super-resolution. Current methods have a tra…
Twisted Schrödinger Bridge Matching
Maxence Noble, Marie Scheid, Yazid Janati +2
Over the past few years, diffusion-based Schrödinger bridge models have been proposed to approximate optimal transport dynamics between two prescribed boundary distributions, with…
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…
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…
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…
Efficient Zero-Shot Inpainting with Decoupled Diffusion Guidance
Badr Moufad, Navid Bagheri Shouraki, Alain Oliviero Durmus +4
Diffusion models have emerged as powerful priors for image editing tasks such as inpainting and local modification, where the objective is to generate realistic content that remain…
Piecewise deterministic generative models
Andrea Bertazzi, Dario Shariatian, Umut Simsekli +2
We introduce a novel class of generative models based on piecewise deterministic Markov processes (PDMPs), a family of non-diffusive stochastic processes consisting of deterministi…
Normalizing constants of log-concave densities
Nicolas Brosse, Alain Durmus, Éric Moulines
We derive explicit bounds for the computation of normalizing constants for log-concave densities with respect to the Lebesgue measure on . Our app…
Blocking Strategies and Stability of Particle Gibbs Samplers
Sumeetpal S. Singh, Fredrik Lindsten, Eric Moulines
Sampling from the conditional (or posterior) probability distribution of the latent states of a Hidden Markov Model, given the realization of the observed process, is a non-trivial…