2 citations · 2 across the 4 of their papers we have counts for
6 papers
From Score Matching to Diffusion: A Fine-Grained Error Analysis in the Gaussian Setting
Samuel Hurault, Matthieu Terris, Thomas Moreau +1
Sampling from an unknown distribution, accessible only through discrete samples, is a fundamental problem at the core of generative AI. The current state-of-the-art methods follow…
Geometry-Aware Discretization Error of Diffusion Models
Samuel Hurault, Thomas Moreau, Gabriel Peyré
Practical diffusion sampling is a numerical approximation problem: under a fixed inference budget, one must simulate a reverse-time ODE or SDE using only a limited number of denois…
Tessellations of Semi-Discrete Flow Matching
Emile Pierret, Johannes Hertrich, Samuel Hurault +1
We study Flow Matching in a semi-discrete setting where a Gaussian source is transported toward a discrete target supported on finitely many points. This semi-discrete regime is th…
Reconstruct Anything Model: a lightweight general model for computational imaging
Matthieu Terris, Samuel Hurault, Maxime Song +1
Most existing learning-based methods for solving imaging inverse problems can be roughly divided into two classes: iterative algorithms, such as plug-and-play and diffusion methods…
DeepInverse: A Python package for solving imaging inverse problems with deep learning
Julián Tachella, Matthieu Terris, Samuel Hurault +24
DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems. The library covers all crucial steps in image reconstruction from the efficient implementa…
Optimization with First Order Algorithms
Charles Dossal, Samuel Hurault, Nicolas Papadakis
These notes focus on the minimization of convex functionals using first-order optimization methods, which are fundamental in many areas of applied mathematics and engineering. The…