M. Abadi, A. Chu, I. Goodfellow, H. B. Mcmahan, I. Mironov et al., Deep Learning with Differential Privacy, Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, CCS'16, pp.308-318, 2016.
DOI : 10.1109/CVPR.2015.7298594
URL : http://arxiv.org/pdf/1607.00133

P. Bernardis and M. Gentilucci, Speech and gesture share the same communication system, Neuropsychologia, vol.44, issue.2, pp.178-190, 2006.
DOI : 10.1016/j.neuropsychologia.2005.05.007

Y. Boureau, J. Ponce, and Y. Lecun, A theoretical analysis of feature pooling in visual recognition, Proceedings of the 27th international conference on machine learning (ICML-10), pp.111-118, 2010.

X. Chen, H. Guo, G. Wang, and L. Zhang, Motion feature augmented recurrent neural network for skeleton-based dynamic hand gesture recognition. arXiv preprint, 2017.
DOI : 10.1109/icip.2017.8296809

K. Cho, B. Van-merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares et al., Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint, 2014.
DOI : 10.3115/v1/d14-1179
URL : https://hal.archives-ouvertes.fr/hal-01433235

E. Cippitelli, S. Gasparrini, E. Gambi, and S. Spinsante, A Human Activity Recognition System Using Skeleton Data from RGBD Sensors, Computational Intelligence and Neuroscience, vol.2, issue.3, article 27, 2016.
DOI : 10.1007/s10994-007-5018-6
URL : http://doi.org/10.1155/2016/4351435

Q. De-smedt, H. Wannous, and J. Vandeborre, Skeleton-Based Dynamic Hand Gesture Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.1-9, 2016.
DOI : 10.1109/CVPRW.2016.153
URL : https://hal.archives-ouvertes.fr/hal-01535152

Q. De-smedt, H. Wannous, J. Vandeborre, J. Guerry, B. L. Saux et al., Shrec'17 track: 3d hand gesture recognition using a depth and skeletal dataset, 10th Eurographics Workshop on 3D Object Retrieval, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01563505

L. Deng and D. Yu, Deep Learning: Methods and Applications, Foundations and Trends?? in Signal Processing, vol.7, issue.3-4, pp.3-4197, 2014.
DOI : 10.1561/2000000039
URL : http://research.microsoft.com/pubs/209355/DeepLearning-NowPublishing-Vol7-SIG-039.pdf

M. Devanne, H. Wannous, S. Berretti, P. Pala, M. Daoudi et al., 3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold, IEEE Transactions on Cybernetics, vol.45, issue.7, pp.1340-1352, 2015.
DOI : 10.1109/TCYB.2014.2350774
URL : https://hal.archives-ouvertes.fr/hal-01056397

X. Glorot and Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp.249-256, 2010.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning, 2016.

N. A. Goussies, S. Ubalde, and M. Mejail, Transfer Learning Decision Forests for Gesture Recognition, The Journal of Machine Learning Research, vol.14, issue.2, pp.3667-3690, 2014.
DOI : 10.1109/CVPR.2010.5539857

A. Graves and N. Jaitly, Towards end-to-end speech recognition with recurrent neural networks, Proceedings of the 31st International Conference on Machine Learning (ICML-14), pp.1764-1772, 2014.

I. Guyon, V. Athitsos, P. Jangyodsuk, H. J. Escalante, and B. Hamner, Results and Analysis of the ChaLearn Gesture Challenge 2012, Advances in Depth Image Analysis and Applications, pp.186-204, 2013.
DOI : 10.1007/978-3-642-40303-3_19

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.770-778, 2016.
DOI : 10.1109/CVPR.2016.90
URL : http://arxiv.org/pdf/1512.03385

C. Hilverman, S. W. Cook, and M. C. Duff, Hippocampal declarative memory supports gesture production: Evidence from amnesia, Cortex, vol.85, pp.25-36, 2016.
DOI : 10.1016/j.cortex.2016.09.015
URL : http://europepmc.org/articles/pmc5127754?pdf=render

S. Hochreiter and J. Schmidhuber, Long Short-Term Memory, Neural Computation, vol.4, issue.8, pp.1735-1780, 1997.
DOI : 10.1016/0893-6080(88)90007-X

D. Kingma and J. Ba, Adam: A method for stochastic optimization. arXiv preprint, 2014.

A. Klaser, M. Marsza?ek, and C. Schmid, A Spatio-Temporal Descriptor Based on 3D-Gradients, Procedings of the British Machine Vision Conference 2008, pp.275-276, 2008.
DOI : 10.5244/C.22.99
URL : https://hal.archives-ouvertes.fr/inria-00514853

J. Knopp, M. Prasad, G. Willems, R. Timofte, and L. Van-gool, Hough Transform and 3D??SURF for Robust Three??Dimensional Classification, pp.589-602, 2010.
DOI : 10.1007/978-3-642-15567-3_43

T. Laurent and J. Von-brecht, A recurrent neural network without chaos, 2017.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, pp.2278-2324, 1998.
DOI : 10.1109/5.726791
URL : http://www.cs.berkeley.edu/~daf/appsem/Handwriting/papers/00726791.pdf

S. Mitra and T. Acharya, Gesture Recognition: A Survey, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.37, issue.3, pp.311-324, 2007.
DOI : 10.1109/TSMCC.2007.893280

P. Molchanov, S. Gupta, K. Kim, and J. Kautz, Hand gesture recognition with 3D convolutional neural networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.1-7, 2015.
DOI : 10.1109/CVPRW.2015.7301342
URL : http://web4.cs.ucl.ac.uk/staff/j.kautz/publications/Gesture_HANDS15.pdf

G. Murthy and R. Jadon, A review of vision based hand gestures recognition, International Journal of Information Technology and Knowledge Management, vol.2, issue.2, pp.405-410, 2009.

N. Neverova, Deep learning for human motion analysis, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01470466

N. Neverova, C. Wolf, G. Paci, G. Sommavilla, G. Taylor et al., A Multi-scale Approach to Gesture Detection and Recognition, 2013 IEEE International Conference on Computer Vision Workshops, pp.484-491, 2013.
DOI : 10.1109/ICCVW.2013.69
URL : https://hal.archives-ouvertes.fr/hal-01339262

N. Neverova, C. Wolf, G. Taylor, and F. Nebout, ModDrop: Adaptive Multi-Modal Gesture Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, issue.8, pp.1692-1706, 2016.
DOI : 10.1109/TPAMI.2015.2461544
URL : https://hal.archives-ouvertes.fr/hal-01178733

E. Ohn-bar and M. Trivedi, Joint Angles Similarities and HOG2 for Action Recognition, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.465-470, 2013.
DOI : 10.1109/CVPRW.2013.76
URL : http://cvrr.ucsd.edu/eshed/papers/OhnBarHAU3D13.pdf

F. J. Ordóñez and D. Roggen, Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors, vol.6, issue.1, p.115, 2016.
DOI : 10.1007/s00779-013-0638-2

O. Oreifej and Z. Liu, HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.716-723, 2013.
DOI : 10.1109/CVPR.2013.98

S. J. Pan and Q. Yang, A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010.
DOI : 10.1109/TKDE.2009.191
URL : http://www.cs.ust.hk/~sinnopan/publications/TLsurvey_0822.pdf

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, vol.1010, issue.1, pp.211-252, 2015.
DOI : 10.1007/978-3-642-15555-0_11
URL : http://dspace.mit.edu/bitstream/1721.1/104944/1/11263_2015_Article_816.pdf

T. Simon, H. Joo, I. Matthews, and Y. Sheikh, Hand Keypoint Detection in Single Images Using Multiview Bootstrapping, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
DOI : 10.1109/CVPR.2017.494

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition. CoRR, abs, 1409.

S. Song, C. Lan, J. Xing, W. Zeng, and J. Liu, An end-to-end spatiotemporal attention model for human action recognition from skeleton data, AAAI, pp.4263-4270, 2017.

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, Journal of machine learning research, vol.15, issue.1, pp.1929-1958, 2014.

A. Truong, H. Boujut, and T. Zaharia, Laban descriptors for gesture recognition and emotional analysis. The visual computer, pp.83-98, 2016.
DOI : 10.1007/s00371-014-1057-8
URL : https://hal.archives-ouvertes.fr/hal-01111425

A. Turkin, Tikhonov regularization for long short-term memory networks. arXiv preprint, 2017.

A. Van-den-oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals et al., Wavenet: A generative model for raw audio. arXiv preprint, 2016.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones et al., Attention is all you need, Advances in Neural Information Processing Systems, pp.6000-6010, 2017.

R. Vemulapalli, F. Arrate, and R. Chellappa, Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.588-595, 2014.
DOI : 10.1109/CVPR.2014.82

P. Wang, Z. Li, Y. Hou, and W. Li, Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks, Proceedings of the 2016 ACM on Multimedia Conference, MM '16, pp.102-106, 2016.
DOI : 10.1109/DICTA.2014.7008101
URL : http://arxiv.org/pdf/1611.02447

Y. Xian, C. H. Lampert, B. Schiele, and Z. Akata, Zero-shot learninga comprehensive evaluation of the good, the bad and the ugly. arXiv preprint, 2017.

Z. Xing, J. Pei, and E. Keogh, A brief survey on sequence classification, ACM SIGKDD Explorations Newsletter, vol.12, issue.1, pp.40-48, 2010.
DOI : 10.1145/1882471.1882478
URL : http://www.cs.sfu.ca/%7Ejpei/publications/Sequence%20Classification.pdf

M. Ye, Q. Zhang, L. Wang, J. Zhu, R. Yang et al., A Survey on Human Motion Analysis from Depth Data, Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications, pp.149-187, 2013.
DOI : 10.1007/978-3-642-44964-2_8
URL : http://www.iai.uni-bonn.de/%7Egall/download/jgall_survey_book13.pdf