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All issues Volume 295 (2024) EPJ Web of Conf., 295 (2024) 09017 References
Open Access
Issue
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
Article Number 09017
Number of page(s) 8
Section Artificial Intelligence and Machine Learning
DOI https://doi.org/10.1051/epjconf/202429509017
Published online 06 May 2024
  1. S. Chatrchyan et al. (CMS), Phys. Lett. B 716, 30 (2012), 1207.7235 [Google Scholar]
  2. G. Aad et al. (ATLAS), Phys. Lett. B 716, 1 (2012), 1207.7214 [Google Scholar]
  3. J. Duarte et al., JINST 13, P07027 (2018), 1804.06913 [CrossRef] [Google Scholar]
  4. CMS Collaboration (CMS), CMS Technical Design Report CERN-LHCC-2020-004. CMS-TDR-021 (2020), https://cds.cern.ch/record/2714892 [Google Scholar]
  5. M.D. Wilkinson, M. Dumontier, I.J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J.W. Boiten, L.B. da Silva Santos, P.E. Bourne et al., Sci. Data 3, 160018 (2016) [NASA ADS] [CrossRef] [Google Scholar]
  6. D.S. Katz, M. Gruenpeter, T. Honeyman, L.J. Hwang, M.D. Wilkinson, V. Sochat, H. Anzt, C.A. Goble (2021), 2101.10883 [Google Scholar]
  7. D.S. Katz, M. Gruenpeter, T. Honeyman, Patterns 2, 100222 (2021) [CrossRef] [PubMed] [Google Scholar]
  8. N.P. Chue Hong, D.S. Katz, M. Barker, A.L. Lamprecht, C. Martinez, F.E. Psomopoulos, J. Harrow, L.J. Castro, M. Gruenpeter, P.A. Martinez et al., FAIR Principles for Research Software (FAIR4RS Principles) (2022) [Google Scholar]
  9. M. Barker, N. Chue Hong, D. Katz, A.L. Lamprecht, C. Martinez Ortiz, F. Psomopoulos, J. Harrow, L. Castro, M. Gruenpeter, P. Martinez et al., Sci. Data 9, 622 (2022) [NASA ADS] [CrossRef] [Google Scholar]
  10. G. Verma, M. Emani, C. Liao, P.H. Lin, T. Vanderbruggen, X. Shen, B. Chapman, HPCFAIR: Enabling FAIR AI for HPC Applications, in 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC) (2021), p. 58 [Google Scholar]
  11. N. Ravi, P. Chaturvedi, E.A. Huerta, Z. Liu, R. Chard, A. Scourtas, K.J. Schmidt, K. Chard, B. Blaiszik, I. Foster, Sci. Data 9, 657 (2022), 2207.00611 [CrossRef] [Google Scholar]
  12. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al., J. Mach. Learn. Res. 12, 2825 (2011) [MathSciNet] [Google Scholar]
  13. M. Abadi et al., TensorFlow: Large-scale machine learning on heterogeneous systems (2015), 1603.04467, https://www.tensorflow.org/ [Google Scholar]
  14. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library, in Advances in Neural Information Processing Systems, edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, R. Garnett (Curran Associates, Inc., 2019), Vol. 32, https://proceedings.neurips.cc/paper/2019/ file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf [Google Scholar]
  15. J. Bai, F. Lu, K. Zhang et al., Open Neural Network Exchange, https://github.com/ onnx/onnx (2017), https://github.com/onnx/onnx [Google Scholar]
  16. S. Wattanakriengkrai, B. Chinthanet, H. Hata, R.G. Kula, C. Treude, J. Guo, K. Matsumoto, J. Syst. Softw. 183, 111117 (2022) [CrossRef] [Google Scholar]
  17. J. Pineau, P. Vincent-Lamarre, K. Sinha, V. Lariviere, A. Beygelzimer, F. d’Alche Buc, E. Fox, H. Larochelle, J. Mach. Learn. Res. 22, 1 (2021), 2003.12206 [Google Scholar]
  18. B. Haibe-Kains, G.A. Adam, A. Hosny, F. Khodakarami, T. Shraddha, R. Kusko, S.A. Sansone, W. Tong, R.D. Wolfinger, C.E. Mason et al., Nature 586, E14 (2020) [CrossRef] [PubMed] [Google Scholar]
  19. K. Sinha et al., ML reproducibility challenge 2022 (2022), https://paperswithcode.com/rc2022 [Google Scholar]
  20. D.S. Katz, Defining FAIR for machine learning (ML) (2021), https://www.rd-alliance.org/defining-fair-machine-learning-ml [Google Scholar]
  21. D.S. Katz, FAIR software and FAIR ML models (2022), https://doi.org/10.5281/ zenodo.6647819 [Google Scholar]
  22. PyTorch Team, PyTorch GitHub Issue #87398: Model outputs different values after ONNX export (2022), https://github.com/pytorch/pytorch/issues/87398\ #issuecomment-1338230472 [Google Scholar]
  23. Driven Data, Cookiecutter data science (2022), https://drivendata.github.io/ cookiecutter-data-science/ [Google Scholar]
  24. FAIR4HEP, Cookiecutter4fair: v1.0.0 (2022), https://github.com/fair4hep/ cookiecutter4fair [Google Scholar]
  25. European Organization For Nuclear Research, OpenAIRE, Zenodo (2013), https://www.zenodo.org/ [Google Scholar]
  26. D. Völgyes, Zenodo_get: A downloader for zenodo records (2020), https://github. com/dvolgyes/zenodo_get [Google Scholar]
  27. Z. Li, R. Chard, L. Ward, K. Chard, T.J. Skluzacek, Y. Babuji, A. Woodard, S. Tuecke, B. Blaiszik, M.J. Franklin et al., J. Parallel. Distrib. Comput. 147, 64 (2021) [CrossRef] [Google Scholar]
  28. K. Chard, M. Lidman, B. McCollam, J. Bryan, R. Ananthakrishnan, S. Tuecke, I. Foster, Future Gener. Comput. Syst. 56, 571 (2016) [CrossRef] [Google Scholar]
  29. J. Vanschoren, J.N. van Rijn, B. Bischl, L. Torgo, SIGKDD Explorations 15, 49 (2013) [Google Scholar]
  30. MLCommons, MLCommons (2022), https://mlcommons.org [Google Scholar]
  31. AI Model Share Project, AI Model Share Platform (2022), https://www. modelshare.org/ [Google Scholar]
  32. Hugging Face, Hugging Face (2024), https://www.huggingface.co/ [Google Scholar]
  33. S. Luccioni, S. Bouchot, C. Akiki, A. Leroy, Introducing DOI: the digital object identifier to datasets and models (2022), https://huggingface.co/blog/ introducing-doi [Google Scholar]
  34. NVIDIA, NVIDIA Triton Inference Server, https://developer.nvidia.com/ nvidia-triton-inference-server (2022) [Google Scholar]
  35. D. Merkel, Linux J. 2014 (2014) [Google Scholar]
  36. G.M. Kurtzer, V. Sochat, M.W. Bauer, PLoS ONE 12 (2017) [Google Scholar]
  37. S. Druskat, J.H. Spaaks, N. Chue Hong, R. Haines, J. Baker, S. Bliven, E. Willighagen, D. Pérez-Suárez, A. Konovalov, Citation File Format (2021), https://citation-file-format.github.io/ [Google Scholar]
  38. P.W. Battaglia, R. Pascanu, M. Lai, D. Rezende, K. Kavukcuoglu, Interaction Networks for Learning about Objects, Relations and Physics, in Advances in Neural Information Processing Systems, edited by D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, R. Garnett (Curran Associates, Inc., 2016), Vol. 29, 1612.00222, https://proceedings.neurips.cc/paper/2016/file/ 3147da8ab4a0437c15ef51a5cc7f2dc4-Paper.pdf [Google Scholar]
  39. E.A. Moreno, T.Q. Nguyen, J.R. Vlimant, O. Cerri, H.B. Newman, A. Periwal, M. Spiropulu, J.M. Duarte, M. Pierini, Phys. Rev. D 102, 012010 (2020), 1909.12285 [CrossRef] [Google Scholar]
  40. J.M. Duarte, B. Li, A. Roy, R. Zhu, Hbb Interaction Network: v0.1.1 (2022), https://github.com/FAIR4HEP/hbb_interaction_network [Google Scholar]
  41. CMS Collaboration, J. Duarte, Sample with jet, track and secondary vertex properties for Hbb tagging ML studies (HiggsToBBNTuple_HiggsToBB_QCD_RunII_13TeV_MC) (2019), CERN Open Data Portal [Google Scholar]
  42. Moreno, E. A., Nguyen, T. Q., Vlimant, J.-R., Cerri, O., Newman, H. B., Periwal, A., Spiropulu, M., Duarte, J. M., Pierini, M., Zhu, R., Roy, A., Huerta, E. A., FAIR Interaction Network Model for Higgs Boson Detection, The Data and Learning Hub for Science (DLHub) (2022) [Google Scholar]
  43. R. Chard, Z. Li, K. Chard, L. Ward, Y. Babuji, A. Woodard, S. Tuecke, B. Blaiszik, M.J. Franklin, I. Foster, DLHub: Model and data serving for science, in 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (IEEE, 2019), p. 283 [Google Scholar]
  44. R. Chard, Y. Babuji, Z. Li, T. Skluzacek, A. Woodard, B. Blaiszik, I. Foster, K. Chard, funcX: A federated function serving fabric for science, in Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing (Association for Computing Machinery, New York, NY, USA, 2020), HPDC ’20, p. 65, ISBN 9781450370523, 2005.04215 [Google Scholar]

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