A lightweight mixup-based short texts clustering for contrastive learning
Traditional text clustering based on distance struggles to distinguish between overlapping representations in medical data. By incorporating contrastive learning, the feature space can be optimized and applies mixup implicitly during the data augmentation phase to reduce computational burden. Medica...
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| Asıl Yazarlar: | , , |
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| Materyal Türü: | Artigo |
| Dil: | Inglês |
| Baskı/Yayın Bilgisi: |
Frontiers Media S.A.
2024-01-01
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| Seri Bilgileri: | Frontiers in Computational Neuroscience |
| Konular: | |
| Online Erişim: | https://www.frontiersin.org/articles/10.3389/fncom.2023.1334748/full |
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