no code implementations • 11 Nov 2023 • Guillaume Quétant, Yury Belousov, Vitaliy Kinakh, Slava Voloshynovskiy
We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods.
no code implementations • 29 Sep 2023 • Erik Buhmann, Cedric Ewen, Darius A. Faroughy, Tobias Golling, Gregor Kasieczka, Matthew Leigh, Guillaume Quétant, John Andrew Raine, Debajyoti Sengupta, David Shih
In addition, we introduce \epcfm, the first permutation equivariant continuous normalizing flow (CNF) for particle cloud generation.
no code implementations • 13 Jul 2023 • Matthew Leigh, Debajyoti Sengupta, John Andrew Raine, Guillaume Quétant, Tobias Golling
Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved diffusion model for the generation of jet particle clouds.
1 code implementation • 9 Mar 2023 • Matthew Leigh, Debajyoti Sengupta, Guillaume Quétant, John Andrew Raine, Knut Zoch, Tobias Golling
In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi.
no code implementations • 20 Dec 2021 • Guillaume Quétant, Mariia Drozdova, Vitaliy Kinakh, Tobias Golling, Slava Voloshynovskiy
We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model.
1 code implementation • 17 Dec 2021 • Vitaliy Kinakh, Mariia Drozdova, Guillaume Quétant, Tobias Golling, Slava Voloshynovskiy
The InfoSCC-GAN architecture is based on an unsupervised contrastive encoder built on the InfoNCE paradigm, an attribute classifier and an EigenGAN generator.