1. Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Softwarehttp://tensorflow.org

2. Ba, J., Caruana, R.: Do deep nets really need to be deep? In: Advances in Neural Information Processing Systems, pp. 2654–2662 (2014)

   Google Scholar 

3. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: 3rd International Conference on Learning Representations, ICLR 2015 (2015)

   Google Scholar 

4. Camgoz, N.C., Hadfield, S., Koller, O., Bowden, R.: SubUNets: end-to-end hand shape and continuous sign language recognition. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3075–3084. IEEE (2017)

   Google Scholar 

5. Chatzis, S.: Indian buffet process deep generative models for semi-supervised classification. In: IEEE ICASSP (2018)

   Google Scholar 

6. Chatzis, S.P., Korkinof, D., Demiris, Y.: A quantum-statistical approach toward robot learning by demonstration. IEEE Trans. Rob.28(6), 1371–1381 (2012)

   Article  Google Scholar 

7. Cho, K., van Merrienboer, B., Gulcehre, C., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. EMNLP (2014)

   Google Scholar 

8. Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprintarXiv:1409.1259 (2014)

9. Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: NIPS 2014 Workshop on Deep Learning, December 2014 (2014)

   Google Scholar 

10. Cihan Camgoz, N., Hadfield, S., Koller, O., Ney, H., Bowden, R.: Neural sign language translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7784–7793 (2018)

    Google Scholar 

11. Dreuw, P., et al.: The signspeak project-bridging the gap between signers and speakers. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC 2010) (2010)

    Google Scholar 

12. Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning.arXiv:1506.02142 (2015)

13. Graves, A.: Practical variational inference for neural networks. In: Proceedings of the NIPS (2011)

    Google Scholar 

14. Griffiths, T.L., Ghahramani, Z.: Infinite latent feature models and the Indian buffet process. In: Proceedings of the NIPS (2005)

    Google Scholar 

15. Guo, D., Zhou, W., Li, H., Wang, M.: Hierarchical LSTM for sign language translation. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

16. Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: NIPS Deep Learning and Representation Learning Workshop (2015)

    Google Scholar 

17. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput.9(8), 1735–1780 (1997)

    Article  Google Scholar 

18. Ishwaran, H., James, L.F.: Gibbs sampling methods for stick-breaking priors. J. Am. Stat. Assoc.96, 161–173 (2001)

    Article MathSciNet  Google Scholar 

19. Jang, E., Gu, S., Poole, B.: Categorical reparameterization using Gumbel-Softmax. In: Proceedings of the ICLR (2017)

    Google Scholar 

20. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprintarXiv:1412.6980 (2014)

21. Kumaraswamy, P.: A generalized probability density function for double-bounded random processes. J. Hydrol.46(1–2), 79–88 (1980)

    Article  Google Scholar 

22. Louizos, C., Ullrich, K., Welling, M.: Bayesian compression for deep learning. In: Proceedings of the NIPS (2017)

    Google Scholar 

23. Luong, M.T., Brevdo, E., Zhao, R.: Neural machine translation (seq2seq) tutorial (2017)

    Google Scholar 

24. Maddison, C., Mnih, A., Teh, Y.: The concrete distribution: a continuous relaxation of discrete random variables. In: International Conference on Learning Representations (2017)

    Google Scholar 

25. Murray, K.: Lack of British sign language interpreters putting deaf people at risk. Guardian8 (2013)

    Google Scholar 

26. Nalisnick, E., Smyth, P.: Stick-breaking variational autoencoders. In: Proceedings of the ICLR (2016)

    Google Scholar 

27. Neklyudov, K., Molchanov, D., Ashukha, A., Vetrov, D.P.: Structured Bayesian pruning via log-normal multiplicative noise. In: Advances in Neural Information Processing Systems, pp. 6775–6784 (2017)

    Google Scholar 

28. Panousis, K., Chatzis, S., Theodoridis, S.: Nonparametric Bayesian deep networks with local competition. In: International Conference on Machine Learning, pp. 4980–4988 (2019)

    Google Scholar 

29. Partaourides, H., Chatzis, S.P.: Asymmetric deep generative models. Neurocomputing241, 90–96 (2017)

    Article  Google Scholar 

30. Partaourides, H., Chatzis, S.P.: Deep network regularization via Bayesian inference of synaptic connectivity. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 30–41. Springer, Cham (2017).https://doi.org/10.1007/978-3-319-57454-7_3

    Chapter  Google Scholar 

31. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

    Google Scholar 

32. Teh, Y.W., Görür, D., Ghahramani, Z.: Stick-breaking construction for the Indian buffet process. In: Proceedings of the AISTATS (2007)

    Google Scholar 

33. Theodoridis, S.: Machine Learning: A Bayesian and Optimization Perspective. Academic Press, Cambridge (2015)

    Google Scholar 

34. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

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