31 January 2022 to 3 February 2022
Embassy Suites by Hilton Newark Wilmington South
US/Eastern timezone

State-of-art deep learning technologies and their application to air-shower reconstruction

2 Feb 2022, 09:45


Talk Wednesday


Vladimir Sotnikov (JetBrains Research)


Once again, the last several years reshaped the state-of-the-art in Computer Vision (CV). Non-convolutional approaches, such as Vision Transformers (ViT) and self-attention multi-layer perceptrons (SA-MLP), are quickly emerging, combined with novel optimization techniques and pre-training methods. Note that ViTs and SA-MLPs are evidently better at incorporating global information about the input data, they're also not spatially invariant, which is more appropriate for the cosmic-ray air-showers detectors. This contribution covers multiple approaches for the unsupervised pre-training - a technique that allows making model learn on the unlabeled (i.e., experimental) data and thus increases the model performance. However, each of the examined approaches is nontrivial to apply to air-showers, which poses a challenge yet to be solved.

Type of Contribution talk

Primary author

Vladimir Sotnikov (JetBrains Research)

Presentation Materials