HPformer : Low-Parameter Transformer With Temporal Dependency Hierarchical Propagation for Health Informatics
Transformers based on Self-Attention (SA) mechanism have demonstrated unrivaled superiority in numerous areas. Compared to RNN-based networks, Transformers can learn the temporal dependency representation of an entire sequence in parallel, while efficiently dealing with long-range dependencies. Howe...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 47(2025), 11 vom: 28. Okt., Seite 10770-10786
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| 1. Verfasser: |
Lee, Wu
(VerfasserIn) |
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| Weitere Verfasser: |
Shi, Yuliang,
Yu, Han,
Cheng, Lin,
Wang, Xinjun,
Yan, Zhongmin,
Kong, Fanyu |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2025
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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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