@proceedings {145,
	title = {Variational Bayesian Sequence-to-Sequence Networks for Memory-Efficient Sign Language Translation},
	journal = {International Symposium on Visual Computing},
	year = {2020},
	pages = {251-262},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {Memory-efficient continuous Sign Language Translation is a significant challenge for the development of assisted technologies with real-time applicability for the deaf. In this work, we introduce a paradigm of designing recurrent deep networks whereby the output of the recurrent layer is derived from appropriate arguments from nonparametric statistics. A novel variational Bayesian sequence-to-sequence network architecture is proposed that consists of a) a full Gaussian posterior distribution for data-driven memory compression and b) a nonparametric Indian Buffet Process prior for regularization applied on the Gated Recurrent Unit non-gate weights. We dub our approach Stick-Breaking Recurrent network and show that it can achieve a substantial weight compression without diminishing modeling performance.},
	isbn = {978-3-030-64559-5},
	author = {Partaourides, Harris and Voskou, Andreas and Kosmopoulos, Dimitrios and Chatzis, Sotirios and N. Metaxas, Dimitris}
}
@conference {85,
	title = {Violence content classification using audio features},
	booktitle = {Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence},
	year = {2006},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin, Heidelberg},
	isbn = {3-540-34117-X, 978-3-540-34117-8},
	author = {Giannakopoulos, Theodoros and Kosmopoulos, Dimitrios and Aristidou, Andreas and Theodoridis, Sergios}
}
