14 citations · 31 across the 6 of their papers we have counts for
6 papers
Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data
Francesco P. Ramunno, S. Hackstein, V. Kinakh +4
Given the rarity of significant solar flares compared to smaller ones, training effective machine learning models for solar activity forecasting is challenging due to insufficient…
TURBO: The Swiss Knife of Auto-Encoders
Guillaume Quétant, Yury Belousov, Vitaliy Kinakh +1
We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods. We start by examining the principles o…
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
Erik Buhmann, Cedric Ewen, Darius A. Faroughy +7
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two nov…
Turbo-Sim: a generalised generative model with a physical latent space
Guillaume Quétant, Mariia Drozdova, Vitaliy Kinakh +2
We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model. By maximising the mutual information…
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
Vitaliy Kinakh, Mariia Drozdova, Guillaume Quétant +2
Conditional generation is a subclass of generative problems where the output of the generation is conditioned by the attribute information. In this paper, we present a stochastic c…
Generation of data on discontinuous manifolds via continuous stochastic non-invertible networks
Mariia Drozdova, Vitaliy Kinakh, Guillaume Quétant +2
The generation of discontinuous distributions is a difficult task for most known frameworks such as generative autoencoders and generative adversarial networks. Generative non-inve…