@incollection{ParaView,
  author    = {James Ahrens and Berk Geveci and Charles Law},
  booktitle = {Visualization Handbook},
  publisher = {Elesvier},
  title     = {{ParaView}: An End-User Tool for Large Data Visualization},
  year      = {2005},
  note      = {{ISBN}~978-0123875822},
  url       = {http://www.paraview.org/}
}

@incollection{NEURIPS2019_9015,
  title     = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
  author    = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
  booktitle = {Advances in Neural Information Processing Systems 32},
  pages     = {8024--8035},
  year      = {2019},
  publisher = {Curran Associates, Inc.},
  url       = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}

@software{Falcon_PyTorch_Lightning_2019,
  author  = {Falcon, William and {The PyTorch Lightning team}},
  doi     = {10.5281/zenodo.3828935},
  license = {Apache-2.0},
  month   = {3},
  title   = {{PyTorch Lightning}},
  url     = {https://github.com/Lightning-AI/lightning},
  version = {1.4},
  year    = {2019}
}

@inproceedings{lhoest-etal-2021-datasets,
  title         = {Datasets: A Community Library for Natural Language Processing},
  author        = {Lhoest, Quentin and {The HuggingFace team}},
  booktitle     = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
  month         = nov,
  year          = {2021},
  address       = {Online and Punta Cana, Dominican Republic},
  publisher     = {Association for Computational Linguistics},
  pages         = {175--184},
  abstract      = {The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets.},
  eprint        = {2109.02846},
  archiveprefix = {arXiv},
  primaryclass  = {cs.CL},
  url           = {https://github.com/huggingface/datasets}
}

@software{von_Platen_Diffusers_State-of-the-art_diffusion,
  author  = {von Platen, Patrick and {The HuggingFace team}},
  license = {Apache-2.0},
  title   = {{Diffusers: State-of-the-art diffusion models}},
  url     = {https://github.com/huggingface/diffusers},
  version = {0.12.1}
}