Differentiable Graph Module (DGM) for Graph Convolutional Networks
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-euclidean structured data. Such methods have shown promising results on a broad spectrum of applications ranging from social science, biomedicine, and particle physics...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 2 vom: 11. Feb., Seite 1606-1617
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| 1. Verfasser: |
Kazi, Anees
(VerfasserIn) |
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| Weitere Verfasser: |
Cosmo, Luca,
Ahmadi, Seyed-Ahmad,
Navab, Nassir,
Bronstein, Michael M |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article |
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