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Communication Dans Un Congrès Année : 2019

Systematic aware learning - A case study in High Energy Physics

Résumé

Experimental science often has to cope with systematic errors that coherently bias data. We analyze this issue on the analysis of data produced by experiments of the Large Hadron Collider at CERN as a case of supervised domain adaptation. Systematics-aware learning should create an efficient representation that is insensitive to perturbations induced by the systematic effects. We present an experimental comparison of the adversarial knowledge-free approach and a less data-intensive alternative.

Dates et versions

hal-02403581 , version 1 (10-12-2019)

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Victor Estrade, Cécile Germain, Isabelle Guyon, David Rousseau. Systematic aware learning - A case study in High Energy Physics. 23rd International Conference on Computing in High Energy and Nuclear Physics, Jul 2018, Sofia, Bulgaria. pp.06024, ⟨10.1051/epjconf/201921406024⟩. ⟨hal-02403581⟩
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