EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 295 (2024) EPJ Web of Conf., 295 (2024) 09029 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 09029
Number of page(s) 8
Section Artificial Intelligence and Machine Learning
DOI https://doi.org/10.1051/epjconf/202429509029
Published online 06 May 2024
  1. K. Aamodt et al. (ALICE), Journal of Instrumentation 3, S08002 (2008) [Google Scholar]
  2. S. Acharya et al. (ALICE), arXiv:2211.04384 [nucl-ex] (2022), 2211.04384 [Google Scholar]
  3. E. Botta (ALICE), Proceeding of the Fifth Annual Conference on Large Hadron Collider Physics (2017), 1709.00288 [Google Scholar]
  4. J. Adam et al. (ALICE), European Physics Journal Plus 131, 168 (2016), 1602.01392 [CrossRef] [Google Scholar]
  5. A. Ghosh, B. Nachman, D. Whiteson, arXiv preprint arXiv:2105.08742 (2021) [Google Scholar]
  6. C. Englert et al., The European Physical Journal C 79, 1 (2019) [CrossRef] [Google Scholar]
  7. AliceO2Group, O2 analysis framework documentation, https://aliceo2group. github.io/analysis-framework/ [Google Scholar]
  8. R. Brun, F. Rademakers, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 389, 81 (1997) [CrossRef] [Google Scholar]
  9. ONNX Community, ONNX, ONNX Community, 2021, https://onnx.ai/ [Google Scholar]
  10. M. Abadi et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems (2015), software available from tensorflow.org [Google Scholar]
  11. A. Paszke et al., in Advances in Neural Information Processing Systems (2019), Vol. 32, pp. 8024–8035 [Google Scholar]
  12. ONNXRuntime Community, ONNXRuntime, ONNXRuntime Community, 2021, https://onnxruntime.ai/ [Google Scholar]
  13. A. Alkin, G. Eulisse, J.F. Grosse-Oetringhaus, P. Hristov, M. Kabus, EPJ Web Conf. 251, 03063 (2021) [Google Scholar]
  14. P.K. Sharpe, R.J. Solly, Neural Computing and Applications 3, 73 (1995) [CrossRef] [Google Scholar]
  15. Z. Wang et al., Attention-based multi-instance neural network for medical diagnosis from incomplete and low quality data, in 2019 International Joint Conference on Neural Networks (IJCNN) (2019), pp. 1–8 [Google Scholar]
  16. D. Grangier, I. Melvin, Advances in Neural Information Processing Systems 23 (2010) [Google Scholar]
  17. A. Vaswani et al., Advances in Neural Information Processing Systems 30 (2017) [Google Scholar]
  18. T. Sjöstrand et al., Computer Physics Communications 191, 159 (2015), 1410.3012 [CrossRef] [Google Scholar]
  19. R. Brun et al., Tech. rep., CERN (1994) [Google Scholar]
  20. J. Blitzer, M. Dredze, F. Pereira, Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification, in Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (2007), pp. 440–447 [Google Scholar]
  21. X. Glorot, A. Bordes, Y. Bengio, Domain adaptation for large-scale sentiment classification: A deep learning approach, in ICML (2011) [Google Scholar]
  22. R. Gopalan, R. Li, R. Chellappa, Domain adaptation for object recognition: An unsupervised approach, in 2011 International Conference on Computer Vision (IEEE, 2011), pp. 999–1006 [Google Scholar]
  23. B. Fernando et al., Unsupervised visual domain adaptation using subspace alignment, in Proceedings of the IEEE International Conference on Computer Vision (2013), pp. 2960–2967 [Google Scholar]
  24. D. Walter, Master thesis (2018) [Google Scholar]
  25. Y. Ganin et al., The Journal of Machine Learning Research 17, 2096 (2016) [Google Scholar]
  26. A. Jha et al., Modelling and Simulation in Materials Science and Engineering 27, 024002 (2019) [CrossRef] [Google Scholar]
  27. Y. Gal, Z. Ghahramani, Dropout as a Bayesian approximation: Representing model uncertainty in deep learning, in International Conference on Machine Learning (PMLR, 2016), pp. 1050–1059 [Google Scholar]
  28. V. Kuleshov, N. Fenner, S. Ermon, Accurate uncertainties for deep learning using calibrated regression, in International Conference on Machine Learning (PMLR, 2018), pp. 2796–2804 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.