7 citations · 16 across the 6 of their papers we have counts for
6 papers
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…
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…
From Denoising Diffusions to Denoising Markov Models
Joe Benton, Yuyang Shi, Valentin De Bortoli +2
Denoising diffusions are state-of-the-art generative models exhibiting remarkable empirical performance. They work by diffusing the data distribution into a Gaussian distribution a…
Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics
Kamélia Daudel, Joe Benton, Yuyang Shi +1
Several algorithms involving the Variational Rényi (VR) bound have been proposed to minimize an alpha-divergence between a target posterior distribution and a variational distribut…
Polysemanticity and Capacity in Neural Networks
Adam Scherlis, Kshitij Sachan, Adam S. Jermyn +2
Individual neurons in neural networks often represent a mixture of unrelated features. This phenomenon, called polysemanticity, can make interpreting neural networks more difficult…
A Continuous Time Framework for Discrete Denoising Models
Andrew Campbell, Joe Benton, Valentin De Bortoli +3
We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and correspondi…