Open Access
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
Article Number 01004
Number of page(s) 7
Section Computer Science and System Design, Application
Published online 23 June 2022
  1. Tenney, Ian, Dipanjan Das, and Ellie Pavlick. “BERT rediscovers the classical NLP pipeline.” arXiv preprint arXiv: 1905. 05950 (2019). [Google Scholar]
  2. Polignano, Marco, et al. “Alberto: Italian BERT language understanding model for NLP challenging tasks based on tweets.” 6th Italian Conference on Computational Linguistics, CLiC-it 2019. Vol. 2481. CEUR, 2019. [Google Scholar]
  3. Gao, Zhengjie, et al. “Target-dependent sentiment classification with BERT.” Ieee Access 7 (2019): 154290–154299. [CrossRef] [Google Scholar]
  4. Masala, Mihai, Stefan Ruseti, and Mihai Dascalu. “Robert–a romanian bert model.” Proceedings of the 28th International Conference on Computational Linguistics. 2020. [Google Scholar]
  5. Moradshahi, Mehrad, et al. “HUBERT untangles BERT to improve transfer across NLP tasks.” arXiv preprint arXiv: 1910. 12647 (2019). [Google Scholar]
  6. Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907. [Google Scholar]
  7. Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., & Welling, M. (2018, June). Modeling relational data with graph convolutional networks. [Google Scholar]
  8. Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., & Weinberger, K. (2019, May). Simplifying graph convolutional networks. In International conference on machine learning (pp. 6861–6871). PMLR. [Google Scholar]
  9. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903. [Google Scholar]
  10. Song, W., Xiao, Z., Wang, Y., Charlin, L., Zhang, M., & Tang, J. (2019, January). Session-based social recommendation via dynamic graph attention networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (pp. 555–563). [Google Scholar]
  11. Zhang, K., Huang, Y., Du, Y., & Wang, L. (2017). Facial expression recognition based on deep evolutional spatial-temporal networks. IEEE Transactions on Image Processing, 26(9), 4193–4203. [Google Scholar]
  12. Cho, Y. S., Galstyan, A., Brantingham, P. J., & Tita, G. (2013). Latent self-exciting point process model for spatial-temporal networks. arXiv preprint arXiv:1302.2671. [Google Scholar]
  13. Wang, Y., Yao, H., & Zhao, S. (2016). Auto-encoder based dimensionality reduction. Neurocomputing, 184, 232–242. [CrossRef] [Google Scholar]
  14. Lange, S., & Riedmiller, M. (2010, July). Deep auto-encoder neural networks in reinforcement learning. In The 2010 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE. [Google Scholar]
  15. Deng, L., Seltzer, M. L., Yu, D., Acero, A., Mohamed, A. R., & Hinton, G. (2010). Binary coding of speech spectrograms using a deep auto-encoder. In Eleventh Annual Conference of the International Speech Communication Association. [Google Scholar]
  16. Sainath, T. N., Kingsbury, B., & Ramabhadran, B. (2012, March). Auto-encoder bottleneck features using deep belief networks. In 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4153–4156). IEEE. [Google Scholar]

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