SPMS 2022

Session

Data Analysis and Deep Learning

26 Jun 2022, 10:45

Conveners

Data Analysis and Deep Learning

• Jiří Franc (Czech Technical University (CZ))

31. Deep Learning in High Energy Physics

Miroslav Kubů (CERN)

26/06/2022, 10:45

Deep learning is a type of a broader family of machine learning models that uses artificial neural networks for extracting abstract features from the raw inputs. High energy physics tasks often profit from the application of deep learning techniques thanks to the amount of training data produced by Monte Carlo simulations and particle detectors. Because of the large amount of processed data,...

33. Ensemble model for detector simulations

Kristina Jarůšková (Department of Software Engineering, FNSPE, Czech Technical University in Prague)

26/06/2022, 11:10

Detector simulations are indispensable for new HEP discoveries. However, the standard step-by-step Monte Carlo simulation tools are very time-consuming and soon will become intractable. In this talk, we present an ensemble model of generative adversarial networks applied to calorimeter images, as an example of a deep learning-based approach to simulations.

18. Deep Learning in Zero-shot Blind Image Deconvolution

Antonie Brožová (Department of Mathematics, FNSPE, Czech Technical University in Prague)

26/06/2022, 11:30

The aim of blind image deconvolution is to recover a sharp image from a blurred one. Assuming that there is no other data than one blurred image, the problem is highly ill-posed. Many methods were proposed, yet there is none that would be 100% reliable. Approaches based on bayesian models that attempt to describe image statistics using priors had played major role in zero-shot blind image...