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Accueil Tous les numéros Volume 225 (2020) EPJ Web Conf., 225 (2020) 01004 Références
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Numéro
EPJ Web Conf.
Volume 225, 2020
ANIMMA 2019 – Advancements in Nuclear Instrumentation Measurement Methods and their Applications
Numéro d'article 01004
Nombre de pages 6
Section Fundamental Physics
DOI https://doi.org/10.1051/epjconf/202022501004
Publié en ligne 20 janvier 2020
  1. T. Zemb and P. Lindner, Neutrons, X-rays and light: scattering methods applied to soft condensed matter. North-Holland, 2002. [Google Scholar]
  2. I. Grillo, “Small-angle neutron scattering and applications in soft condensed matter”, Soft matter characterization, pp. 723–782, 2008. [CrossRef] [Google Scholar]
  3. J. Schmidhuber, “Deep learning in neural networks: An overview”, Neural networks, vol. 61, pp. 85–117, 2015. [Google Scholar]
  4. S. Lathuiliére, P. Mesejo, X. Alameda-Pineda, and R. Horaud, “A comprehensive analysis of deep regression”, IEEE transactions on pattern analysis and machine intelligence, 2019. [Google Scholar]
  5. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks” in Advances in neural information processing systems, 2012, pp. 1097–1105. [Google Scholar]
  6. U. R. Acharya, S. L. Oh, Y. Hagiwara, J. H. Tan, and H. Adeli, “Deep convolutional neural network for the automated detection and diagnosis of seizure using eeg signals”, Computers in biology and medicine, vol. 100, pp. 270–278, 2018. [CrossRef] [PubMed] [Google Scholar]
  7. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2014, pp. 1725–1732. [Google Scholar]
  8. Y. Kim, “Convolutional neural networks for sentence classification” arXiv preprint arXiv:1408.5882, 2014. [Google Scholar]
  9. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Advances in neural information processing systems” Curran Associates, Inc, pp. 1097–1105 2012. [Google Scholar]
  10. H. Tang, A. M. Scaife, and J. Leahy, “Transfer learning for radio galaxy classification”, arXiv preprint arXiv:1903.11921, 2019. [Google Scholar]
  11. S. Khan, N. Islam, Z. Jan, I. U. Din, and J. J. C. Rodrigues, “A novel deep learning based framework for the detection and classification of breast cancer using transfer learning”, Pattern Recognition Letters, 2019. [PubMed] [Google Scholar]
  12. A. Kaya, A. S. Keceli, C. Catal, H. Y. Yalic, H. Temucin, and B. Tekinerdogan, “Analysis of transfer learning for deep neural network based plant classification models”, Computers and Electronics in Agriculture, vol. 158, pp. 20–29, 2019. [Google Scholar]
  13. “Grasp”, https://www.ill.eu/fr/users-en/scientific-groups/large-scalestructures/grasp/, institut Laue-Langevin. [Google Scholar]
  14. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision”, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818–2826. [Google Scholar]
  15. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1–9. [Google Scholar]
  16. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition”, arXiv preprint arXiv:1409.1556, 2014. [Google Scholar]
  17. M. Talo, U. B. Baloglu, Ö. Yıldırım, and U. R. Acharya, “Application of deep transfer learning for automated brain abnormality classificationusing mr images”, Cognitive Systems Research, vol. 54, pp. 176–188, 2019. [Google Scholar]