M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis et al., Tensorflow: A system for large-scale machine learning, OSDI, vol.16, pp.265-283, 2016.

H. Abbas, A. Rodionova, E. Bartocci, S. A. Smolka, and R. Grosu, Quantitative regular expressions for arrhythmia detection algorithms, Int. Conf. on Computational Methods in Systems Biology, pp.23-39, 2017.

R. Alur, D. Fisman, and M. Raghothaman, Regular programming for quantitative properties of data streams, LNCS. Springer, vol.9632, pp.15-40, 2016.

E. Arafailova, N. Beldiceanu, R. Douence, M. Carlsson, P. Flener et al., Global constraint catalog, volume ii, time-series constraints, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01374721

N. Beldiceanu, M. Carlsson, R. Douence, and H. Simonis, Using finite transducers for describing and synthesising structural time-series constraints, Constraints, vol.21, issue.1, pp.22-40, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01186662

N. Beldiceanu, G. Ifrim, A. Lenoir, and H. Simonis, Describing and generating solutions for the EDF unit commitment problem with the modelseeker, LNCS. Springer, vol.8124, pp.733-748, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00874326

L. Bottou, Stochastic gradient descent tricks, Neural networks: Tricks of the trade, pp.421-436, 2012.

M. Carlsson, J. Widen, J. Andersson, S. Andersson, K. Boortz et al., SICStus Prolog user's manual, vol.3, 1988.

R. Caruana and A. Niculescu-mizil, An empirical comparison of supervised learning algorithms, Proceedings of the 23rd international conference on Machine learning, pp.161-168, 2006.

A. Coates and A. Y. Ng, Learning Feature Representations with KMeans, 2012.

M. A. Francisco-rodríguez, P. Flener, and J. Pearson, Automatic generation of descriptions of time-series constraints, 2017.

T. Hastie, R. Tibshirani, and J. Friedman, Unsupervised learning, The elements of statistical learning, pp.485-585, 2009.

P. G. Ipeirotis, F. Provost, and J. Wang, Quality management on Amazon Mechanical Turk, Proceedings of the ACM SIGKDD workshop on human computation, pp.64-67, 2010.

A. K. Jain, Data clustering: 50 years beyond k-means, Pattern recognition letters, vol.31, issue.8, pp.651-666, 2010.

G. Madi-wamba, Y. Li, A. Orgerie, N. Beldiceanu, and J. Menaud, Green energy aware scheduling problem in virtualized datacenters, ICPADS 2017, pp.648-655, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01582936

M. A. Nielsen, Neural networks and deep learning, 2015.

G. Paolacci, J. Chandler, and P. G. Ipeirotis, Running experiments on amazon mechanical turk, 2010.

F. Scholkmann, J. Boss, and M. Wolf, An efficient algorithm for automatic peak detection in noisy periodic and quasi-periodic signals, Algorithms, vol.5, issue.4, pp.588-603, 2012.

R. Tibshirani, G. Walther, and T. Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, pp.411-423, 2001.

A. M. Turk, Amazon Mechanical Turk, 2012.

K. V. Vishwanath and N. Nagappan, Characterizing cloud computing hardware reliability, Proceedings of the 1st ACM symposium on Cloud computing, pp.193-204, 2010.