Balanced Marginal and Joint Distributional Learning for Tabular Data Synthesis via Mixture Cramer–Wold Distance
In recent times, slicing methods have yielded a successful outcome in generative models for image, sound, and text data, primarily focusing on joint distributional learning. However, we have identified a critical limitation of the slicing approach for tabular data: it struggles to capture marginal d...
I tiakina i:
| Ngā kaituhi matua: | , , |
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| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
MDPI AG
2026-03-01
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| Rangatū: | Applied Sciences |
| Ngā marau: | |
| Urunga tuihono: | https://www.mdpi.com/2076-3417/16/6/2928 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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