Measurements of the higgs boson production and decay rates and constraints on its couplings from a combined atlas and cms analysis of the lhc pp collision data at ? s = 7 ,
URL : https://hal.archives-ouvertes.fr/in2p3-01328829
The Higgs boson machine learning challenge, HEPML@ NIPS, pp.19-55, 2014. ,
DOI : 10.1088/1742-6596/664/7/072015
URL : https://hal.archives-ouvertes.fr/in2p3-01024802
Searching for exotic particles in high-energy physics with deep learning, Nature Communications, vol.ACAT, pp.7-2014 ,
DOI : 10.1103/PhysRevLett.102.152001
Systematic Errors: facts and fictions. ArXiv High Energy Physics -Experiment e-prints, 2002. ,
A theory of learning from different domains, Machine Learning, vol.60, issue.1-2, pp.1-2151, 2010. ,
DOI : 10.1007/s10994-009-5152-4
Representation Learning: A Review and New Perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1798-1828, 2013. ,
DOI : 10.1109/TPAMI.2013.50
URL : http://www.cs.princeton.edu/courses/archive/spring13/cos598C/Representation Learning - A Review and New Perspectives.pdf
Censoring Representations with an Adversary, International Conference in Learning Representations (ICLR2016), 2016. ,
Domain-Adversarial Training of Neural Networks, 2015. ,
DOI : 10.1007/978-3-319-58347-1_10
URL : https://hal.archives-ouvertes.fr/hal-01624607
Learning to Pivot with Adversarial Networks, physics, 2016. ,
The Manifold Tangent Classifier, NIPS, p.523, 2011. ,
Contractive autoencoders: Explicit invariance during feature extraction, Proceedings of the 28th international conference on machine learning (ICML-11), pp.833-840, 2011. ,
Tangent Prop -A Formalism for Specifying Selected Invariances in an Adaptive Network, NIPS, pp.895-903, 1991. ,
Adversarial perturbations of deep neural networks, Perturbation, Optimization and Statistics, pp.1-32, 2016. ,