11 citations · 27 across the 6 of their papers we have counts for
6 papers
Deep learning for track recognition in pixel and strip-based particle detectors
O. Bakina, D. Baranov, I. Denisenko +11
The reconstruction of charged particle trajectories in tracking detectors is a key problem in the analysis of experimental data for high-energy and nuclear physics. The amount of d…
Quality and Cost Trade-offs in Passage Re-ranking Task
Pavel Podberezko, Vsevolod Mitskevich, Raman Makouski +4
Deep learning models named transformers achieved state-of-the-art results in a vast majority of NLP tasks at the cost of increased computational complexity and high memory consumpt…
Ariadne: PyTorch Library for Particle Track Reconstruction Using Deep Learning
Pavel Goncharov, Egor Schavelev, Anastasia Nikolskaya +1
Particle tracking is a fundamental part of the event analysis in high energy and nuclear physics. Events multiplicity increases each year along with the drastic growth of the exper…
One-Shot Learning with Triplet Loss for Vegetation Classification Tasks
Alexander Uzhinskiy, Gennady Ososkov, Pavel Goncharov +2
Triplet loss function is one of the options that can significantly improve the accuracy of the One-shot Learning tasks. Starting from 2015, many projects use Siamese networks and t…
The particle track reconstruction based on deep learning neural networks
Dmitriy Baranov, Sergey Mitsyn, Pavel Goncharov +1
One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which cons…
Catch and Prolong: recurrent neural network for seeking track-candidates
Dmitriy Baranov, Gennady Ososkov, Pavel Goncharov +1
One of the most important problems of data processing in high energy and nuclear physics is the event reconstruction. Its main part is the track reconstruction procedure which cons…