Search Results for author: Guillaume Quétant

Found 6 papers, 2 papers with code

TURBO: The Swiss Knife of Auto-Encoders

no code implementations11 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.

EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion

no code implementations29 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.

PC-Droid: Faster diffusion and improved quality for particle cloud generation

no code implementations13 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.

Turbo-Sim: a generalised generative model with a physical latent space

no code implementations20 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.

Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN

1 code implementation17 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.

Attribute Generative Adversarial Network +1

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