Zobrazeno 1 - 10
of 203
pro vyhledávání: '"Deja Kamil"'
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09029 (2024)
The main focus of the ALICE experiment, quark–gluon plasma measurements, requires accurate particle identification (PID). The ALICE subdetectors allow identifying particles over a broad momentum interval ranging from about 100 MeV/c up to 20 GeV/c.
Externí odkaz:
https://doaj.org/article/847fe3aa34d044e186aa9aaec0df063f
Recent personalization methods for diffusion models, such as Dreambooth, allow fine-tuning pre-trained models to generate new concepts. However, applying these techniques across multiple tasks in order to include, e.g., several new objects or styles,
Externí odkaz:
http://arxiv.org/abs/2410.04891
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 Monte-Carlo simulations strains CERN's computational grid, underscori
Externí odkaz:
http://arxiv.org/abs/2406.03263
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 simulation methods have garnered attention as promising alternatives to trad
Externí odkaz:
http://arxiv.org/abs/2406.03233
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 been sparked by the development of collision simulations at the Large Had
Externí odkaz:
http://arxiv.org/abs/2405.14049
Publikováno v:
JINST 19, C07013 (2024)
The ALICE experiment at the LHC measures properties of the strongly interacting matter formed in ultrarelativistic heavy-ion collisions. Such studies require accurate particle identification (PID). ALICE provides PID information via several detectors
Externí odkaz:
http://arxiv.org/abs/2403.17436
We introduce GUIDE, a novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten. Existing generative strategies combat catastrophic forgetting by randomly sampling rehearsal examples from a generat
Externí odkaz:
http://arxiv.org/abs/2403.03938
Autor:
Kasak, Miłosz, Deja, Kamil, Karwowska, Maja, Jakubowska, Monika, Graczykowski, Łukasz, Janik, Małgorzata
Publikováno v:
Eur.Phys.J.C 84 (2024) 7, 691
In this work, we introduce a novel method for Particle Identification (PID) within the scope of the ALICE experiment at the Large Hadron Collider at CERN. Identifying products of ultrarelativisitc collisions delivered by the LHC is one of the crucial
Externí odkaz:
http://arxiv.org/abs/2401.01905
In this work, we introduce Adapt & Align, a method for continual learning of neural networks by aligning latent representations in generative models. Neural Networks suffer from abrupt loss in performance when retrained with additional training data
Externí odkaz:
http://arxiv.org/abs/2312.13699
Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian
Externí odkaz:
http://arxiv.org/abs/2310.12001