442 citations · 3.3k across the 135 of their papers we have counts for
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Nearly -Linear Convergence Bounds for Diffusion Models via Stochastic Localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet +1
Denoising diffusions are a powerful method to generate approximate samples from high-dimensional data distributions. Recent results provide polynomial bounds on their convergence r…
A Unified Framework for U-Net Design and Analysis
Christopher Williams, Fabian Falck, George Deligiannidis +3
U-Nets are a go-to, state-of-the-art neural architecture across numerous tasks for continuous signals on a square such as images and Partial Differential Equations (PDE), however t…
Error Bounds for Flow Matching Methods
Joe Benton, George Deligiannidis, Arnaud Doucet
Score-based generative models are a popular class of generative modelling techniques relying on stochastic differential equations (SDE). From their inception, it was realized that…
Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters
Maxence Noble, Valentin De Bortoli, Arnaud Doucet +1
Multi-marginal Optimal Transport (mOT), a generalization of OT, aims at minimizing the integral of a cost function with respect to a distribution with some prescribed marginals. In…
Trans-Dimensional Generative Modeling via Jump Diffusion Models
Andrew Campbell, William Harvey, Christian Weilbach +3
We propose a new class of generative models that naturally handle data of varying dimensionality by jointly modeling the state and dimension of each datapoint. The generative proce…
Diffusion Schrödinger Bridge Matching
Yuyang Shi, Valentin De Bortoli, Andrew Campbell +1
Solving transport problems, i.e. finding a map transporting one given distribution to another, has numerous applications in machine learning. Novel mass transport methods motivated…