2 citations · 6 across the 8 of their papers we have counts for
8 papers
DSS-GAN: Directional State Space GAN with Mamba backbone for Class-Conditional Image Synthesis
Aleksander Ogonowski, Konrad Klimaszewski, Przemysław Rokita
We present DSS-GAN, the first generative adversarial network to employ Mamba as a hierarchical generator backbone for noise-to-image synthesis. The central contribution is Directio…
ExpertSim: Fast Particle Detector Simulation Using Mixture-of-Generative-Experts
Patryk Będkowski, Jan Dubiński, Filip Szatkowski +3
Simulating detector responses is a crucial part of understanding the inner workings of particle collisions in the Large Hadron Collider at CERN. Such simulations are currently perf…
Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN
Patryk Będkowski, Jan Dubiński, Kamil Deja +1
Simulating detector responses is a crucial part of understanding the inner-workings of particle collisions in the Large Hadron Collider at CERN. The current reliance on statistical…
Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN
Mikołaj Kita, Jan Dubiński, Przemysław Rokita +1
In High Energy Physics simulations play a crucial role in unraveling the complexities of particle collision experiments within CERN's Large Hadron Collider. Machine learning simula…
Particle physics DL-simulation with control over generated data properties
Karol Rogoziński, Jan Dubiński, Przemysław Rokita +1
The research of innovative methods aimed at reducing costs and shortening the time needed for simulation, going beyond conventional approaches based on Monte Carlo methods, has bee…
Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN
Jan Dubiński, Kamil Deja, Sandro Wenzel +2
Currently, over half of the computing power at CERN GRID is used to run High Energy Physics simulations. The recent updates at the Large Hadron Collider (LHC) create the need for d…