127 citations · 461 across the 16 of their papers we have counts for
16 papers
Denoising Diffusion Samplers
Francisco Vargas, Will Grathwohl, Arnaud Doucet
Denoising diffusion models are a popular class of generative models providing state-of-the-art results in many domains. One adds gradually noise to data using a diffusion to transf…
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
Yilun Du, Conor Durkan, Robin Strudel +6
Since their introduction, diffusion models have quickly become the prevailing approach to generative modeling in many domains. They can be interpreted as learning the gradients of…
Continuous diffusion for categorical data
Sander Dieleman, Laurent Sartran, Arman Roshannai +11
Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement. Their success hinges…
Self-conditioned Embedding Diffusion for Text Generation
Robin Strudel, Corentin Tallec, Florent Altché +8
Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation? To circumvent the discrete nature of text data, we can si…
Learning to Navigate Wikipedia by Taking Random Walks
Manzil Zaheer, Kenneth Marino, Will Grathwohl +7
A fundamental ability of an intelligent web-based agent is seeking out and acquiring new information. Internet search engines reliably find the correct vicinity but the top results…
Score-Based Diffusion meets Annealed Importance Sampling
Arnaud Doucet, Will Grathwohl, Alexander G. D. G. Matthews +1
More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains one of the most effective methods for marginal likelihood estimation. It relies on a seque…