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SUMMARY:State-of-art deep learning technologies and their application to a
 ir-shower reconstruction
DTSTART:20220202T144500Z
DTEND:20220202T153000Z
DTSTAMP:20260807T122600Z
UID:indico-contribution-7975@events.icecube.wisc.edu
DESCRIPTION:Speakers: Vladimir Sotnikov (JetBrains Research)\n\nOnce again
 \, the last several years reshaped the state-of-the-art in Computer Vision
  (CV). Non-convolutional approaches\, such as Vision Transformers (ViT) an
 d self-attention multi-layer perceptrons (SA-MLP)\, are quickly emerging\,
  combined with novel optimization techniques and pre-training methods. Not
 e that ViTs and SA-MLPs are evidently better at incorporating global infor
 mation about the input data\, they're also not spatially invariant\, which
  is more appropriate for the cosmic-ray air-showers detectors. This contri
 bution covers multiple approaches for the unsupervised pre-training - a te
 chnique that allows making model learn on the unlabeled (i.e.\, experiment
 al) data and thus increases the model performance. However\, each of the e
 xamined approaches is nontrivial to apply to air-showers\, which poses a c
 hallenge yet to be solved.\n\nhttps://events.icecube.wisc.edu/event/141/co
 ntributions/7975/
LOCATION:Online
RELATED-TO:indico-event-141@events.icecube.wisc.edu
URL:https://events.icecube.wisc.edu/event/141/contributions/7975/
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