Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015.

T. Mantecón, D. Casals, J. J. Navarro-corcuera, C. R. Blanco, and F. Jaureguizar, Deep Learning to Enhance Maritime Situation Awareness, 2019 20th International Radar Symposium (IRS), pp.1-8, 2019.

Y. Chen, Y. S. Wang, and E. A. Erosheva, On the use of bootstrap with variational inference: Theory, interpretation, and a two-sample test example, The Annals of Applied Statistics, vol.12, issue.2, pp.846-876, 2018.

E. N. Souza, K. Boerder, S. Matwin, and B. Worm, Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning, PLOS ONE, vol.11, issue.7, p.158248, 2016.

N. Forti, L. M. Millefiori, P. Braca, and P. Willett, PREDICTION OF VESSEL TRAJECTORIES FROM AIS DATA VIA SEQUENCE-TO-SEQUENCE RECURRENT NEURAL NETWORKS

D. Nguyen, R. Vadaine, G. Hajduch, R. Garello, and R. Fablet, A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams, 2018 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Oct, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01808176

, GeoTrackNet-A Maritime Anomaly Detector using Probabilistic Neural Network Representation of AIS Tracks and A Contrario Detection, 2019.

J. Chung, K. Kastner, L. Dinh, K. Goel, A. Courville et al., A Recurrent Latent Variable Model for Sequential Data, Advances in neural information processing systems, pp.2980-2988, 2015.

I. M. , International Convention on the Prevention of Pollution from Ships -MARPOL, Regulation 13

D. Balouek, A. C. Amarie, G. Charrier, F. Desprez, E. Jeannot et al., Adding Virtualization Capabilities to the Grid'5000 Testbed, Cloud Computing and Services Science, ser. Communications in Computer and Information Science, pp.3-20, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00946971