Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physicsTani, Laurits; Veelken, ChristianComputer physics communications2024 / p. 108955 https://doi.org/10.1016/j.cpc.2023.108955

Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics : [preprint]Tani, Laurits; Veelken, ChristianarXiv.org2024 / 8 p. : ill https://doi.org/10.48550/arXiv.2201.06809

Doubly charged Higgs boson decays and implications on neutrino physics = Kahekordse laenguga Higgsi bosoni lagunemiste analüüs ja selle mõju neutriinofüüsikaleKadastik, Mario2008 https://www.ester.ee/record=b2424087*est

Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physicsTani, Laurits; Rand, Diana; Veelken, Christian; Kadastik, MarioThe European Physical Journal C2021 / art. 170, 9 p. : ill https://doi.org/10.1140/epjc/s10052-021-08950-y

Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics : [preprint]Tani, Laurits; Rand, Diana; Veelken, Christian; Kadastik, MarioarXiv.org2021 / 11 p. : ill https://doi.org/10.48550/arXiv.2011.04434

Measurement of Higgs boson properties in leptonic final states using ML-methods = Higgsi bosoni omaduste mõõtmine leptoneid sisaldavates kanalites kasutades masinõppe meetodeidTani, Laurits2024 https://www.ester.ee/record=b5685160*est https://doi.org/10.23658/taltech.25/2024 https://digikogu.taltech.ee/et/Item/95677715-7be7-4696-a8a6-cdbe321761b2

Palatini F (R, X) : a new framework for inflationary attractorsDioguardi, Christian; Racioppi, AntonioarXiv2023 / 6 p. : ill https://doi.org/10.48550/arXiv.2307.02963