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Springe zu: Konferenzbeitrag
Anzahl der Publikationen: 2
Konferenzbeitrag
Damke, Clemens 
ORCID: https://orcid.org/0000-0002-0455-0048; Melnikov, Vitalik und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(November 2020):
A Novel Higher-order Weisfeiler-Lehman Graph Convolution.
12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 18-20 November 2020.
Jialin Pan, Sinno und Sugiyama, Masashi (Hrsg.):
In: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020),
Bd. 129
S. 49-64
ORCID: https://orcid.org/0000-0002-0455-0048; Melnikov, Vitalik und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(November 2020):
A Novel Higher-order Weisfeiler-Lehman Graph Convolution.
12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 18-20 November 2020.
Jialin Pan, Sinno und Sugiyama, Masashi (Hrsg.):
In: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020),
Bd. 129
S. 49-64
Tornede, Alexander ORCID: https://orcid.org/0000-0002-2415-2186; Wever, Marcel ORCID: https://orcid.org/0000-0001-9782-6818; Werner, Stefan; Mohr, Felix und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(November 2020):
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis.
12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 18-20 November 2020.
Jialin Pan, Sinno und Sugiyama, Masashi (Hrsg.):
In: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020),
Bd. 129
S. 737-752
ORCID: https://orcid.org/0000-0002-2415-2186; Wever, Marcel ORCID: https://orcid.org/0000-0001-9782-6818; Werner, Stefan; Mohr, Felix und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(November 2020):
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis.
12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 18-20 November 2020.
Jialin Pan, Sinno und Sugiyama, Masashi (Hrsg.):
In: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020),
Bd. 129
S. 737-752