Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Lartigue, Thomas"'
Autor:
Lartigue, Thomas, Mukherjee, Sach
In many applications, data can be heterogeneous in the sense of spanning latent groups with different underlying distributions. When predictive models are applied to such data the heterogeneity can affect both predictive performance and interpretabil
Externí odkaz:
http://arxiv.org/abs/2205.01486
Autor:
Lartigue, Thomas, Mukherjee, Sach
Linear projections are widely used in the analysis of high-dimensional data. In unsupervised settings where the data harbour latent classes/clusters, the question of whether class discriminatory signals are retained under projection is crucial. In th
Externí odkaz:
http://arxiv.org/abs/2204.05139
Publikováno v:
SN Computer Science, Springer, 2021, 2 (466), \&\#x27E8;10.1007/s42979-021-00865-5\&\#x27E9
Conditional correlation networks, within Gaussian Graphical Models (GGM), are widely used to describe the direct interactions between the components of a random vector. In the case of an unlabelled Heterogeneous population, Expectation Maximisation (
Externí odkaz:
http://arxiv.org/abs/2006.11094
Publikováno v:
Algorithms, MDPI, 2022, Stochastic Algorithms and Their Applications, 15 (3), pp.78
The Expectation Maximisation (EM) algorithm is widely used to optimise non-convex likelihood functions with latent variables. Many authors modified its simple design to fit more specific situations. For instance, the Expectation (E) step has been rep
Externí odkaz:
http://arxiv.org/abs/2003.10126
Autor:
Lartigue, Thomas, Bottani, Simona, Baron, Stephanie, Colliot, Olivier, Durrleman, Stanley, Allassonnière, Stéphanie
Gaussian Graphical Models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM inference methods: nodewise edge selection and penalised likelihood max
Externí odkaz:
http://arxiv.org/abs/2003.05169
Regularized regression models are well studied and, under appropriate conditions, offer fast and statistically interpretable results. However, large data in many applications are heterogeneous in the sense of harboring distributional differences betw
Externí odkaz:
http://arxiv.org/abs/1908.07869
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Koval, Igor, Bône, Alexandre, Louis, Maxime, Lartigue, Thomas, Bottani, Simona, Marcoux, Arnaud, Samper-Gonzalez, Jorge, Burgos, Ninon, Charlier, Benjamin, Bertrand, Anne, Epelbaum, Stéphane, Colliot, Olivier, Allassonnière, Stéphanie, Durrleman, Stanley
Publikováno v:
Scientific Reports
Scientific Reports, Nature Publishing Group, 2021, 11 (1), ⟨10.1038/s41598-021-87434-1⟩
Scientific Reports, 2021, 11 (1), ⟨10.1038/s41598-021-87434-1⟩
Scientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
Scientific Reports, Nature Publishing Group, 2021, 11 (1), ⟨10.1038/s41598-021-87434-1⟩
Scientific Reports, 2021, 11 (1), ⟨10.1038/s41598-021-87434-1⟩
Scientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
International audience; Alzheimer's disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid_dedup__::5ae2c66bd32116dcbf44b6d874ae2956
https://hal.inria.fr/hal-01964821
https://hal.inria.fr/hal-01964821