@article{SteppaHolch2019,
  author    = {Steppa, Constantin Beverly and Holch, Tim Lukas},
  title     = {HexagDLy-Processing hexagonally sampled data with CNNs in PyTorch},
  series = {SoftwareX},
  volume    = {9},
  journal   = {SoftwareX},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {2352-7110},
  doi       = {10.1016/j.softx.2019.02.010},
  pages     = {193 -- 198},
  year      = {2019},
  abstract  = {HexagDLy is a Python-library extending the PyTorch deep learning framework with convolution and pooling operations on hexagonal grids. It aims to ease the access to convolutional neural networks for applications that rely on hexagonally sampled data as, for example, commonly found in ground-based astroparticle physics experiments.},
  language  = {en}
}