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HexagDLy-Processing hexagonally sampled data with CNNs in PyTorch
- 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.
| Author details: | Constantin Beverly SteppaORCiD, Tim Lukas HolchORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1016/j.softx.2019.02.010 |
| ISSN: | 2352-7110 |
| Title of parent work (English): | SoftwareX |
| Publisher: | Elsevier |
| Place of publishing: | Amsterdam |
| Publication type: | Article |
| Language: | English |
| Year of first publication: | 2019 |
| Publication year: | 2019 |
| Release date: | 2021/05/17 |
| Tag: | Astroparticle physics; Convolutional neural networks; Hexagonal grid; PyTorch |
| Volume: | 9 |
| Number of pages: | 6 |
| First page: | 193 |
| Last Page: | 198 |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
| Peer review: | Referiert |
| Publishing method: | Open Access / Gold Open-Access |
| DOAJ gelistet | |
| License (German): |  CC-BY - Namensnennung 4.0 International |
