research
∙
08/27/2023
Diffusion Schrödinger Bridges for Bayesian Computation
Denoising diffusion models are a novel class of generative models that h...
share
research
∙
08/07/2023
Linear Convergence Bounds for Diffusion Models via Stochastic Localization
Diffusion models are a powerful method for generating approximate sample...
research
∙
07/11/2023
Metropolis Sampling for Constrained Diffusion Models
Denoising diffusion models have recently emerged as the predominant para...
research
∙
07/11/2023
Geometric Neural Diffusion Processes
Denoising diffusion models have proven to be a flexible and effective pa...
research
∙
06/15/2023
Unbalanced Diffusion Schrödinger Bridge
Schrödinger bridges (SBs) provide an elegant framework for modeling the ...
research
∙
05/26/2023
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters
Multi-marginal Optimal Transport (mOT), a generalization of OT, aims at ...
research
∙
05/25/2023
Trans-Dimensional Generative Modeling via Jump Diffusion Models
We propose a new class of generative models that naturally handle data o...
research
∙
04/11/2023
Diffusion Models for Constrained Domains
Denoising diffusion models are a recent class of generative models which...
research
∙
03/29/2023
Diffusion Schrödinger Bridge Matching
Solving transport problems, i.e. finding a map transporting one given di...
research
∙
02/05/2023
SE(3) diffusion model with application to protein backbone generation
The design of novel protein structures remains a challenge in protein en...
research
∙
11/07/2022
From Denoising Diffusions to Denoising Markov Models
Denoising diffusions are state-of-the-art generative models which exhibi...
research
∙
10/21/2022
Barrier Hamiltonian Monte Carlo
In this paper, we propose Barrier Hamiltonian Monte Carlo (BHMC), a vers...
research
∙
09/28/2022
Spectral Diffusion Processes
Score-based generative modelling (SGM) has proven to be a very effective...
research
∙
09/16/2022
Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows
Solving Fredholm equations of the first kind is crucial in many areas of...
research
∙
08/10/2022
Convergence of denoising diffusion models under the manifold hypothesis
Denoising diffusion models are a recent class of generative models exhib...
research
∙
08/09/2022
Wavelet Score-Based Generative Modeling
Score-based generative models (SGMs) synthesize new data samples from Ga...
research
∙
07/07/2022
Riemannian Diffusion Schrödinger Bridge
Score-based generative models exhibit state of the art performance on de...
research
∙
06/29/2022
Can Push-forward Generative Models Fit Multimodal Distributions?
Many generative models synthesize data by transforming a standard Gaussi...
research
∙
05/30/2022
A Continuous Time Framework for Discrete Denoising Models
We provide the first complete continuous time framework for denoising di...
research
∙
02/27/2022
Conditional Simulation Using Diffusion Schrödinger Bridges
Denoising diffusion models have recently emerged as a powerful class of ...
research
∙
02/06/2022
Riemannian Score-Based Generative Modeling
Score-based generative models (SGMs) are a novel class of generative mod...
research
∙
01/16/2022
On Maximum-a-Posteriori estimation with Plug Play priors and stochastic gradient descent
Bayesian methods to solve imaging inverse problems usually combine an ex...
research
∙
11/14/2021
Simulating Diffusion Bridges with Score Matching
We consider the problem of simulating diffusion bridges, i.e. diffusion ...
research
∙
10/25/2021
On quantitative Laplace-type convergence results for some exponential probability measures, with two applications
Laplace-type results characterize the limit of sequence of measures (π_ε...
research
∙
08/18/2021
Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure
We establish the uniform in time stability, w.r.t. the marginals, of the...
research
∙
06/01/2021
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
Progressively applying Gaussian noise transforms complex data distributi...
research
∙
03/08/2021
Bayesian imaging using Plug Play priors: when Langevin meets Tweedie
Since the seminal work of Venkatakrishnan et al. (2013), Plug Play (...
research
∙
08/13/2020
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach. Part II: Theoretical Analysis
This paper presents a detailed theoretical analysis of the three stochas...
research
∙
07/13/2020
Quantitative Propagation of Chaos for SGD in Wide Neural Networks
In this paper, we investigate the limiting behavior of a continuous-time...
research
∙
04/08/2020
Continuous and Discrete-Time Analysis of Stochastic Gradient Descent for Convex and Non-Convex Functions
This paper proposes a thorough theoretical analysis of Stochastic Gradie...
research
∙
12/03/2019
Maximum entropy methods for texture synthesis: theory and practice
Recent years have seen the rise of convolutional neural network techniqu...
research
∙
11/26/2019
Maximum likelihood estimation of regularisation parameters in high-dimensional inverse problems: an empirical Bayesian approach
Many imaging problems require solving an inverse problem that is ill-con...
research
∙
10/28/2019
Approximate Bayesian Computation with the Sliced-Wasserstein Distance
Approximate Bayesian Computation (ABC) is a popular method for approxima...
research
∙
06/28/2019
