P. Diederik, M. Kingma, and . Welling, Auto-encoding variational bayes, 2013.

D. Jimenez-rezende, S. Mohamed, and D. Wierstra, Stochastic backpropagation and approximate inference in deep generative models, Proceedings of the 31st International Conference on Machine Learning, vol.32, pp.1278-1286, 2014.

K. Sohn, H. Lee, and X. Yan, Learning structured output representation using deep conditional generative models, Advances in neural information processing systems, pp.3483-3491, 2015.

S. Chatrchyan, The CMS experiment at the CERN LHC, JINST, vol.3, p.8004, 2008.
URL : https://hal.archives-ouvertes.fr/in2p3-00311605

L. The and . Group, The Large Hadron Collider, conceptual design, 1995.

. Vardan-khachatryan, The CMS trigger system, JINST, vol.12, issue.01, p.1020, 2017.

I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot et al., Shakir Mohamed, and Alexander Lerchner. beta-vae: Learning basic visual concepts with a constrained variational framework, vol.ICLR, p.6, 2017.

M. Gemici, C. Hung, A. Santoro, G. Wayne, S. Mohamed et al., Generative temporal models with memory, 2017.

S. Durk-p-kingma, D. Mohamed, M. Jimenez-rezende, and . Welling, Semi-supervised learning with deep generative models, Advances in neural information processing systems, pp.3581-3589, 2014.

F. Chollet, , 2015.

M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., Large-scale machine learning on heterogeneous distributed systems, 2016.

P. Diederik, J. Kingma, and . Ba, Adam: A method for stochastic optimization, 2014.

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P. Manzagol, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, Journal of machine learning research, vol.11, pp.3371-3408, 2010.