Explainability in Graph Neural Networks : A Taxonomic Survey
Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain predictions, giving rise to the...
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Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 5 vom: 19. Mai, Seite 5782-5799
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| 1. Verfasser: |
Yuan, Hao
(VerfasserIn) |
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| Weitere Verfasser: |
Yu, Haiyang,
Gui, Shurui,
Ji, Shuiwang |
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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
Research Support, U.S. Gov't, Non-P.H.S. |
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