Zobrazeno 1 - 10
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pro vyhledávání: '"Claassen, T."'
Autor:
Schoenmacker, G.H., Groenman, A.P., Sokolova, E., Oosterlaan, J., Rommelse, N., Roeyers, H., Oades, R.D., Faraone, S.V., Franke, B., Heskes, T., Arias Vasquez, A., Claassen, T., Buitelaar, J.K.
Publikováno v:
In European Neuropsychopharmacology January 2020 30:102-113
Autor:
T Claassen T Claassen, Sonja Verwey
Publikováno v:
Communicare: Journal for Communication Studies in Africa. 17:73-89
The first part of this article provides an overview of communicationmanagement as an organisation function, and in doing so adopts asystems approach showing the interdependency of the variousSUbsystems. The role of the communication manager as channe
Autor:
Claassen, T., Bucur, I.G.
Publikováno v:
Proceedings of Machine Learning Research, 180, 443-452
Proceedings of Machine Learning Research, 180, pp. 443-452
Proceedings of Machine Learning Research, 180, pp. 443-452
Contains fulltext : 253540.pdf (Publisher’s version ) (Closed access) Contains fulltext : 253540.pdf (Author’s version preprint ) (Open Access)
Publikováno v:
Jaeger, M. (ed.), PGM 2020: The 10th International Conference on Probabilistic Graphical Models Aalborg, September 23-25, 2020, 1-12. S.l. : MLR Press
STARTPAGE=1;ENDPAGE=12;ISSN=2640-3498;TITLE=Jaeger, M. (ed.), PGM 2020: The 10th International Conference on Probabilistic Graphical Models Aalborg, September 23-25, 2020
Jaeger, M. (ed.), PGM 2020: The 10th International Conference on Probabilistic Graphical Models Aalborg, September 23-25, 2020, pp. 1-12
STARTPAGE=1;ENDPAGE=12;ISSN=2640-3498;TITLE=Jaeger, M. (ed.), PGM 2020: The 10th International Conference on Probabilistic Graphical Models Aalborg, September 23-25, 2020
Jaeger, M. (ed.), PGM 2020: The 10th International Conference on Probabilistic Graphical Models Aalborg, September 23-25, 2020, pp. 1-12
Contains fulltext : 228693.pdf (Publisher’s version ) (Open Access) PGM 2020
Akademický článek
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Publikováno v:
Jonas, P.; David, S. (ed.), Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 1159-1168
Jonas, P.; David, S. (ed.), Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), 1159-1168. [S.l.] : PMLR
STARTPAGE=1159;ENDPAGE=1168;ISSN=2640-3498;TITLE=Jonas, P.; David, S. (ed.), Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)
Jonas, P.; David, S. (ed.), Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), 1159-1168. [S.l.] : PMLR
STARTPAGE=1159;ENDPAGE=1168;ISSN=2640-3498;TITLE=Jonas, P.; David, S. (ed.), Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)
While feedback loops are known to play important roles in many complex systems, their existence is ignored in a large part of the causal discovery literature, as systems are typically assumed to be acyclic from the outset. When applying causal discov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5d0dc522f70b4829920b234a14d4dcd
https://hdl.handle.net/2066/225704
https://hdl.handle.net/2066/225704
Autor:
Mooij, J.M., Claassen, T.
Publikováno v:
Proceedings of Machine Learning Research, 124, 1159-1168
While feedback loops are known to play important roles in many complex systems, their existence is ignored in a large part of the causal discovery literature, as systems are typically assumed to be acyclic from the outset. When applying causal discov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::19f1bbb08160cd6da137b6819270fa94
https://dare.uva.nl/personal/pure/en/publications/constraintbased-causal-discovery-with-partial-ancestral-graphs-in-the-presence-of-cycles(2ec109d4-52dd-4de8-a14f-50f4e54de4a3).html
https://dare.uva.nl/personal/pure/en/publications/constraintbased-causal-discovery-with-partial-ancestral-graphs-in-the-presence-of-cycles(2ec109d4-52dd-4de8-a14f-50f4e54de4a3).html
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
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Autor:
Magliacane, S., van Ommen, T., Claassen, T., Bongers, S., Versteeg, P., Mooij, J.M., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R.
Publikováno v:
32nd Conference on Neural Information Processing Systems 2018: Montreal, Canada, 3-8 December 2018, 15, 10846-10856
An important goal common to domain adaptation and causal inference is to make accurate predictions when the distributions for the source (or training) domain(s) and target (or test) domain(s) differ. In many cases, these different distributions can b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::810ec404fcd7540cd27af831d4bfe951
https://dare.uva.nl/personal/pure/en/publications/domain-adaptation-by-using-causal-inference-to-predict-invariant-conditional-distributions(23b6cc25-8d96-4784-bc8c-0c8205dc4b71).html
https://dare.uva.nl/personal/pure/en/publications/domain-adaptation-by-using-causal-inference-to-predict-invariant-conditional-distributions(23b6cc25-8d96-4784-bc8c-0c8205dc4b71).html
Publikováno v:
Kratochvíl, V.; Studený, M. (ed.), International Conference on Probabilistic Graphical Models, 11-14 September 2018, Prague, Czech Republic, pp. 37-48
Kratochvíl, V.; Studený, M. (ed.), International Conference on Probabilistic Graphical Models, 11-14 September 2018, Prague, Czech Republic, 37-48. [S.l.] : PMLR
STARTPAGE=37;ENDPAGE=48;ISSN=2640-3498;TITLE=Kratochvíl, V.; Studený, M. (ed.), International Conference on Probabilistic Graphical Models, 11-14 September 2018, Prague, Czech Republic
Kratochvíl, V.; Studený, M. (ed.), International Conference on Probabilistic Graphical Models, 11-14 September 2018, Prague, Czech Republic, 37-48. [S.l.] : PMLR
STARTPAGE=37;ENDPAGE=48;ISSN=2640-3498;TITLE=Kratochvíl, V.; Studený, M. (ed.), International Conference on Probabilistic Graphical Models, 11-14 September 2018, Prague, Czech Republic
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::863a8cde512e0731b22c8620eee3ddd7
http://arxiv.org/abs/1809.06827
http://arxiv.org/abs/1809.06827