Výpis publikací

POL, Adrian Alan, Virginia AZZOLINI, Gianluca CERMINARA, Frederico DE GUIO, Giovanni FRANZONI, Maurizio PIERINI, Filip ŠIROKÝ a Jean-Roch VLIMANT. Anomaly detection using Deep Autoencoders for the assessment of the quality of the data acquired by the CMS experiment. Online. In 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019). Francie: EDP Sciences, 2019, s. 1-5. ISSN 2100-014X. Dostupné z: https://dx.doi.org/10.1051/epjconf/201921406008.

ŠIROKÝ, Filip. Anomaly Detection Using Deep Sparse Autoencoders for CERN Particle Detector Data. 2019.

AZZOLINI, V., M. BORISYAK, G. CERMINARA, D. DERKACH, G. FRANZONI, F. DE GUIO, O. GUIO, M. KOVAL, M. PIERINI, A. POL, F. RATNIKOV, Filip ŠIROKÝ, A. USTYUZHANIN a J-R. VLIMANT. Deep learning for inferring cause of data anomalies. In Journal of Physics: Conference Series Volume 1085, Issue 4, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017. Seattle: Institute of Physics Publishing, 2018, s. 1-6. ISSN 1742-6588. Dostupné z: https://dx.doi.org/10.1088/1742-6596/1085/4/042015.

ALBERTSSON, Kim, Piero ALTOE, Dustin ANDERSON, Michael ANDREWS, Juan Pedro Araque ESPINOSA, Adam AURISANO, Laurent BASARA, Adrian BEVAN, Wahid BHIMJI, Daniele BONACORSI, Paolo CALAFIURA, Mario CAMPANELLI, Louis CAPPS, Federico CARMINATI, Stefano CARRAZZA, Taylor CHILDERS, Elias CONIAVITIS, Kyle CRANMER, Claire DAVID, Douglas DAVIS, Javier DUARTE, Martin ERDMANN, Jonas ESCHLE, Amir FARBIN, Matthew FEICKERT, Nuno Filipe CASTRO, Conor FITZPATRICK, Michele FLORIS, Alessandra FORTI, Jordi GARRA-TICO, Jochen GEMMLER, Maria GIRONE, Paul GLAYSHER, Sergei GLEYZER, Vladimir GLIGOROV, Tobias GOLLING, Jonas GRAW, Lindsey GRAY, Dick GREENWOOD, Thomas HACKER, John HARVEY, Benedikt HEGNER, Lukas HEINRICH, Ben HOOBERMAN, Johannes JUNGGEBURTH, Michael KAGAN, Meghan KANE, Konstantin KANISHCHEV, Przemys l aw Karpi n SKI, Zahari KASSABOV, Gaurav KAUL, Dorian KCIRA, Thomas KECK, Alexei KLIMENTOV, Jim KOWALKOWSKI, Luke KRECZKO, Alexander KUREPIN, Rob KUTSCHKE, Valentin KUZNETSOV, Nicolas KÖHLER, Igor LAKOMOV, Kevin LANNON, Mario LASSNIG, Antonio LIMOSANI, Gilles LOUPPE, Aashrita MANGU, Pere MATO, Helge MEINHARD, Dario MENASCE, Lorenzo MONETA, Seth MOORTGAT, Meenakshi NARAIN, Mark NEUBAUER, Harvey NEWMAN, Hans PABST, Michela PAGANINI, Manfred PAULINI, Gabriel PERDUE, Uzziel PEREZ, Attilio PICAZIO, Jim PIVARSKI, Harrison PROSPER, Fernanda PSIHAS, Alexander RADOVIC, Ryan REECE, Aurelius RINKEVICIUS, Eduardo RODRIGUES, Jamal RORIE, David ROUSSEAU, Aaron SAUERS, Steven SCHRAMM, Ariel SCHWARTZMAN, Horst SEVERINI, Paul SEYFERT, Filip ŠIROKÝ, Konstantin SKAZYTKIN, Mike SOKOLOFF, Graeme STEWART, Bob STIENEN, Ian STOCKDALE, Giles STRONG, Savannah THAIS, Karen TOMKO, Eli UPFAL, Emanuele USAI, Andrey USTYUZHANIN, Martin VALA, Sofia VALLECORSA, Justin VASEL, Mauro VERZETTI, Xavier VILASIS-CARDONA, Jean-Roch VLIMANT, Ilija VUKOTIC, Sean-Jiun WANG, Gordon WATTS, Michael WILLIAMS, Wenjing WU, Stefan WUNSCH a Omar ZAPATA. Machine Learning in High Energy Physics Community White Paper. In Journal of Physics: Conference Series Volume 1085, Issue 4, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017. Seattle: Institute of Physics Publishing, 2018, s. 1-6. ISSN 1742-6588. Dostupné z: https://dx.doi.org/10.1088/1742-6596/1085/2/022008.