Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| GRU Inayofunzwa kwa Udhaifu× | Transformer ya Usimamizi dhaifu× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili≠ | 2014–2016 | 2017–2019 |
| Mwanzilishi≠ | Chung et al. (GRU); Ratner et al. (weak supervision framework) | Multiple contributors (weak supervision paradigm: Zhou 2018; transformer backbone: Vaswani et al. 2017) |
| Aina≠ | Weakly supervised sequence model | Weakly supervised deep learning |
| Chanzo asilia≠ | Ratner, A. J., De Sa, C. M., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29. link ↗ | Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., & Re, C. (2017). Snorkel: Rapid training data creation with weak supervision. Proceedings of the VLDB Endowment, 11(3), 269–282. DOI ↗ |
| Majina mbadala | WS-GRU, GRU with weak supervision, weakly labeled GRU, noisy-label GRU | WST, weakly supervised attention model, noisy-label transformer, weak supervision with transformers |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | Weakly Supervised GRU trains a Gated Recurrent Unit network on sequences labeled by imperfect, heuristic, or programmatic sources rather than costly hand-annotated ground truth. It combines the GRU's efficiency at capturing temporal dependencies with weak-supervision techniques that aggregate noisy labels, enabling practical sequence modeling when large fully labeled datasets are unavailable. | Weakly Supervised Transformer combines the representational power of Transformer architectures with weak supervision strategies that exploit noisy, incomplete, or programmatically generated labels — making it possible to train high-quality NLP and vision models when fully annotated datasets are scarce or prohibitively expensive to produce. |
| ScholarGateSeti ya data ↗ |
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