EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 196 (2024) SHS Web Conf., 196 (2024) 02002 References
Open Access
Issue
SHS Web Conf.
Volume 196, 2024
2024 International Conference on Economic Development and Management Applications (EDMA2024)
Article Number 02002
Number of page(s) 10
Section Finance and Stock Market
DOI https://doi.org/10.1051/shsconf/202419602002
Published online 26 August 2024
  1. McCarthy, J. (2007). What Is Artificial Intelligence? [Online] Available at: http://www-formal.stanford.edu/jmc/whatisai/whatisai.html [Google Scholar]
  2. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. [CrossRef] [Google Scholar]
  3. Haykin, S. (2009). Neural Networks and Learning Machines. Pearson. [Google Scholar]
  4. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444. [CrossRef] [PubMed] [Google Scholar]
  5. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. [CrossRef] [Google Scholar]
  6. Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press. [Google Scholar]
  7. Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., … & Kingsbury, B. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups. IEEE Signal Processing Magazine, 29(6), 82-97. [CrossRef] [Google Scholar]
  8. Kumar, S., & Thennarasu, K. (2015). Application of back propagation neural network for predicting the type and onset of dementia from clinical scales. Journal of Clinical and Diagnostic Research, 9(1), YC01. [Google Scholar]
  9. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … & Bengio, Y. (2014). Generative Adversarial Nets. In Advances in Neural Information Processing Systems (pp. 2672-2680). [Google Scholar]
  10. Larsen, A. B. L., Sønderby, S. K., Larochelle, H., & Winther, O. (2015). Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300. [Google Scholar]
  11. Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press. [Google Scholar]
  12. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. [CrossRef] [Google Scholar]
  13. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. [Google Scholar]
  14. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. [CrossRef] [PubMed] [Google Scholar]
  15. Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179-211. [CrossRef] [Google Scholar]
  16. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. [CrossRef] [Google Scholar]
  17. Haykin, S. (2009). Neural Networks and Learning Machines. Pearson. [Google Scholar]
  18. LeCun, Y. (1988). A theoretical framework for back-propagation. In Proceedings of the 1988 connectionist models summer school (pp. 21-28). [Google Scholar]
  19. Werbos, P. J. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Ph.D. thesis, Harvard University. [Google Scholar]
  20. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS 2014), 2672-2680. [Google Scholar]
  21. Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv preprint arXiv:1511.06434. [Google Scholar]
  22. Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV). [Google Scholar]
  23. Segler, T. H., Kogej, T., Tyrchan, C., & Waller, M. P. (2018). Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Central Science, 4(1), 120-131. [CrossRef] [Google Scholar]
  24. Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., & Metaxas, D. N. (2018). StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV). [Google Scholar]
  25. Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. arXiv preprint arXiv:1701.07875. [Google Scholar]
  26. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved Training of Wasserstein GANs. Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017), 5767-5777. [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.