Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

• Autores: Nuruzzaman, Perdue G.N., Ghosh A., Wospakrik M., Akbar F., Andrade D.A., Ascencio M., Bellantoni L., Bercellie A., Betancourt M., Vera G.F.R.C., Cai T., Carneiro M.F., Chaves J., Coplowe D., Motta H.D., Díaz G.A., Felix J., Fields L., Fine R., Gago A.M., Galindo R., Golan T., Gran R., Han J.Y., Harris D.A., Jena D., Kleykamp J., Kordosky M., Lu X.-G., Maher E., Mann W.A., Marshall C.M., McFarland K.S., McGowan A.M., Messerly B., Miller J., Nelson J.K., Nguyen C., Norrick A., Nuruzzaman N., Olivier A., Patton R., Ramírez M.A., Ransome R.D., Ray H., Ren L., Rimal D., Ruterbories D., Schellman H., Salinas C.J.S., Su H., Upadhyay S., Valencia E., Wolcott J., Yaeggy B., Young S.
• Formato: artículo
• Fecha de Publicación: 2018
• Institución: Consejo Nacional de Ciencia Tecnología e Innovación
• Repositorio: CONCYTEC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.concytec.gob.pe:20.500.12390/587
• Enlace del recurso: https://hdl.handle.net/20.500.12390/587; https://doi.org/10.1088/1748-0221/13/11/P11020
• Nivel de acceso: acceso abierto
• Materia: Physics programs; Neural networks; Neutrons; Pattern recognition; Deep convolutional neural networks; Learning classifiers; Learning-based methods; Neutrino detectors; Neutrino interactions; Performance degradation; https://purl.org/pe-repo/ocde/ford#3.01.00

Construction support also was granted by the United States National Science Foundation under Award PHY-0619727 and by the University of Rochester. Additional support for participating scientists was provided by NSF and DOE (U.S.A.) by CAPES and CNPq (Brazil), by CoNaCyT (Mexico), by Proyecto Basal FB 0821, CONICYT PIA ACT1413, Fondecyt 3170845 and 11130133 (Chile), by PIIC (DGIP-UTFSM), by CONCYTEC, DGI-PUCP and IDI/IGI-UNI (Peru)