16 citations · 39 across the 10 of their papers we have counts for
3 papers · 1 filter
On conditional diffusion models for PDE simulations
Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin +4
Modelling partial differential equations (PDEs) is of crucial importance in science and engineering, and it includes tasks ranging from forecasting to inverse problems, such as dat…
Denoising Diffusion Probabilistic Models in Six Simple Steps
Richard E. Turner, Cristiana-Diana Diaconu, Stratis Markou +3
Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including imag…
Transformer Neural Autoregressive Flows
Massimiliano Patacchiola, Aliaksandra Shysheya, Katja Hofmann +1
Density estimation, a central problem in machine learning, can be performed using Normalizing Flows (NFs). NFs comprise a sequence of invertible transformations, that turn a comple…