1 code implementation • 8 Feb 2024 • Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar
We explain this observation by showing that time series from these datasets tend to be more localized in the frequency domain than in the time domain, which makes them easier to model in the former case.
no code implementations • 27 Nov 2023 • Teo Deveney, Jan Stanczuk, Lisa Maria Kreusser, Chris Budd, Carola-Bibiane Schönlieb
In this paper we rigorously describe the range of dynamics and approximations that arise when training score-based diffusion models, including the true SDE dynamics, the neural approximations, the various approximate particle dynamics that result, as well as their associated Fokker--Planck equations and the neural network approximations of these Fokker--Planck equations.
no code implementations • 24 Apr 2023 • Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb
This issue stems from the unrealistic assumption that approximates the conditional data distribution, $p(\textbf{x} | \textbf{z})$, as an isotropic Gaussian.
no code implementations • 23 Dec 2022 • Jan Stanczuk, Georgios Batzolis, Teo Deveney, Carola-Bibiane Schönlieb
A diffusion model approximates the score function i. e. the gradient of the log density of a noise-corrupted version of the target distribution for varying levels of corruption.
no code implementations • 20 Jul 2022 • Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann
We show that non-uniform diffusion leads to multi-scale diffusion models which have similar structure to this of multi-scale normalizing flows.
no code implementations • 16 Jun 2022 • Tolou Shadbahr, Michael Roberts, Jan Stanczuk, Julian Gilbey, Philip Teare, Sören Dittmer, Matthew Thorpe, Ramon Vinas Torne, Evis Sala, Pietro Lio, Mishal Patel, AIX-COVNET Collaboration, James H. F. Rudd, Tuomas Mirtti, Antti Rannikko, John A. D. Aston, Jing Tang, Carola-Bibiane Schönlieb
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial.
1 code implementation • 26 Nov 2021 • Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling.
no code implementations • 2 Mar 2021 • Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser, Carola-Bibiane Schönlieb
Wasserstein GANs are based on the idea of minimising the Wasserstein distance between a real and a generated distribution.