Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Melkas, Laila"'
Autor:
Melkas, Laila, Savvides, Rafael, Chandramouli, Suyog, Mäkelä, Jarmo, Nieminen, Tuomo, Mammarella, Ivan, Puolamäki, Kai
Causal structure discovery (CSD) models are making inroads into several domains, including Earth system sciences. Their widespread adaptation is however hampered by the fact that the resulting models often do not take into account the domain knowledg
Externí odkaz:
http://arxiv.org/abs/2107.01126
Autor:
Honkela, Antti, Melkas, Laila
Gaussian processes (GPs) are non-parametric Bayesian models that are widely used for diverse prediction tasks. Previous work in adding strong privacy protection to GPs via differential privacy (DP) has been limited to protecting only the privacy of t
Externí odkaz:
http://arxiv.org/abs/2106.00474
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Mäkelä, Jarmo, Melkas, Laila, Mammarella, Ivan, Nieminen, Tuomo, Chandramouli, Suyog, Savvides, Rafael, Puolamäki, Kai
This is a comment on "Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach" by Krich et al., Biogeosciences, 17, 1033–1061, 2020, which gives a good introduction to causal discovery, but confines the scope by in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=copernicuspu::ce7a6ce82c282854aa3fc33d7d10ec44
https://bg.copernicus.org/preprints/bg-2021-231/
https://bg.copernicus.org/preprints/bg-2021-231/