2 citations · 2 across the 4 of their papers we have counts for
5 papers
Weighted Adjoint Matching: Finetuning Flow Matching Models with Time-Dependent Control Cost
Eliot Beyler, Francis Bach
We introduce Weighted Adjoint Matching, a method to finetune a pretrained diffusion or flow matching model given a differentiable reward. We derive it from Adjoint Matching, which…
Convergence of Deterministic and Stochastic Diffusion-Model Samplers: A Simple Analysis in Wasserstein Distance
Eliot Beyler, Francis Bach
We provide new convergence guarantees in Wasserstein distance for diffusion-based generative models, covering both stochastic (DDPM-like) and deterministic (DDIM-like) sampling met…
Optimal Denoising in Score-Based Generative Models: The Role of Data Regularity
Eliot Beyler, Francis Bach
Score-based generative models achieve state-of-the-art sampling performance by denoising a distribution perturbed by Gaussian noise. In this paper, we focus on a single determinist…
Sampling Binary Data by Denoising through Score Functions
Francis Bach, Saeed Saremi
Gaussian smoothing combined with a probabilistic framework for denoising via the empirical Bayes formalism, i.e., the Tweedie-Miyasawa formula (TMF), are the two key ingredients in…
Variational Inference on the Boolean Hypercube with the Quantum Entropy
Eliot Beyler, Francis Bach
In this paper, we derive variational inference upper-bounds on the log-partition function of pairwise Markov random fields on the Boolean hypercube, based on quantum relaxations of…