28 citations · 29 across the 4 of their papers we have counts for
4 papers
Approach to Finding a Robust Deep Learning Model
Alexey Boldyrev, Fedor Ratnikov, Andrey Shevelev
The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of large numbers of models. This growing demand highlights the im…
First branching fraction measurement of the suppressed decay
LHCb collaboration, R. Aaij, C. Abellán Beteta +963
The baryon is unstable and usually decays into charmless final states by the transition. It can, however, also disintegrate into a meson and a…
ML-assisted versatile approach to Calorimeter R&D
Alexey Boldyrev, Denis Derkach, Fedor Ratnikov +1
Advanced detector R&D for both new and ongoing experiments in HEP requires performing computationally intensive and detailed simulations as part of the detector-design optimisation…
Using machine learning to speed up new and upgrade detector studies: a calorimeter case
F. Ratnikov, D. Derkach, A. Boldyrev +3
In this paper, we discuss the way advanced machine learning techniques allow physicists to perform in-depth studies of the realistic operating modes of the detectors during the sta…