EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 448 (2023) E3S Web Conf., 448 (2023) 02025 References
Open Access
Issue
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
Article Number 02025
Number of page(s) 10
Section Information System
DOI https://doi.org/10.1051/e3sconf/202344802025
Published online 17 November 2023
  1. A Zhang, C., & Kovacs, J. M. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6), 693-712. (2012) [CrossRef] [Google Scholar]
  2. O'Shea, T. J., Roy, T., & Clancy, T. C. Over-the-Air Deep Learning Based Radio Signal Classification. IEEE Journal of Selected Topics in Signal Processing, 12(1), 168-179. (2016) [Google Scholar]
  3. Merchant, K., Revay, S., Stantchev, G., & Nousain, B. Deep learning for RF device fingerprinting in cognitive communication networks. IEEE journal of selected topics in signal processing, 12(1), 160-167. (2018) [CrossRef] [Google Scholar]
  4. Ruder, S. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. (2016) [Google Scholar]
  5. Kingma, D. P., & Ba, J. Adam: A Method for Stochastic Optimization. arXiv preprint arXiv:1412.6980. (2014) [Google Scholar]
  6. Sutskever, I., Martens, J., Dahl, G., & Hinton, G. On the importance of initialization and momentum in deep learning. In Proceedings of the 30th International Conference on Machine Learning (ICML 2013) (pp. 1139-1147). (2013) [Google Scholar]
  7. Sokolova, M., & Lapalme, G. A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427-437 (2009) [CrossRef] [Google Scholar]
  8. Ruder, Sebastian. “An overview of gradient descent optimization algorithms.” arXiv preprint arXiv:1609.04747 (2016) [Google Scholar]
  9. Reddi, Sashank J., et al. “On the convergence of Adam and beyond.” Proceedings of the 6th International Conference on Learning Representations (ICLR) (2018) [Google Scholar]
  10. Zeiler, Matthew D. “ADADELTA: an adaptive learning rate method.” arXiv preprint arXiv:1212.5701 (2012). [Google Scholar]
  11. Vasilev, Ivan, et al. Python Deep Learning: Exploring deep learning techniques and neural network architectures with Pytorch, Keras, and TensorFlow. Packt Publishing Ltd (2019). [Google Scholar]
  12. Lu, Le, et al. “Deep learning and convolutional neural networks for medical image computing.” Advances in computer vision and pattern recognition 10: 978-3 (2017). [Google Scholar]
  13. Agarap, Abien Fred. “Deep learning using rectified linear units (relu).” arXiv preprint arXiv:1803.08375 (2018). [Google Scholar]
  14. Pangestu, Muftah Afrizal, and Hendra Bunyamin. “Analisis Performa dan Pengembangan Sistem Deteksi Ras Anjing pada Gambar dengan Menggunakan Pre-Trained CNN Model.” Jurnal Teknik Informatika dan Sistem Informasi 4.2: 341-348 (2018). [Google Scholar]
  15. Wang, Meiqi, et al. “A high-speed and low-complexity architecture for softmax function in deep learning.” 2018 IEEE asia pacific conference on circuits and systems (APCCAS). IEEE (2018). [Google Scholar]
  16. Shazeer, Noam, and Mitchell Stern. “Adafactor: Adaptive learning rates with sublinear memory cost.” International Conference on Machine Learning. PMLR (2018). [Google Scholar]
  17. Duchi, John, Elad Hazan, and Yoram Singer. “Adaptive subgradient methods for online learning and stochastic optimization.” Journal of machine learning research 12.7 (2011). [Google Scholar]
  18. Loshchilov, Ilya, and Frank Hutter. “Fixing weight decay regularization in adam.” (2017). [Google Scholar]
  19. Dozat, Timothy. “Incorporating nesterov momentum into adam.” (2016). [Google Scholar]
  20. Tieleman, Tijmen, and Geoffrey Hinton. “Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.” COURSERA: Neural networks for machine learning 4.2: 26-31 (2012). [Google Scholar]
  21. Bottou, Léon. “Large-scale machine learning with stochastic gradient descent.” Proceedings of COMPSTAT'2010: 19th International Conference on Computational StatisticsParis France, August 22-27, 2010 Keynote, Invited and Contributed Papers. Physica-Verlag HD (2010). [Google Scholar]
  22. Aliyu, Ibrahim, Yong Beom Lim, and Chang Gyoon Lim. “Epilepsy detection in EEG signal using recurrent neural network.” Proceedings of the 2019 3rd International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (2019). [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.