Linganisha mbinu

	GRU Inayofunzwa kwa Udhaifu	LSTM iliyo na Usimamizi dhaifu
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2014–2016	2016–2018
Mwanzilishi	Chung et al. (GRU); Ratner et al. (weak supervision framework)	Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)
Aina	Weakly supervised sequence model	Weakly supervised sequence model
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.	Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.
Majina mbadala	WS-GRU, GRU with weak supervision, weakly labeled GRU, noisy-label GRU	WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM
Zinazohusiana	6	6
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 LSTM trains a Long Short-Term Memory network on sequence data where clean, manually annotated labels are scarce or absent. Instead, multiple imperfect label sources — heuristic rules, distant supervision, crowdsourcing, or programmatic labeling functions — are combined to produce probabilistic training labels, which are then used to supervise the LSTM. This allows scalable training on large unlabeled corpora without exhaustive human annotation.