Compară metode

	LSTM slab supraveghere slabă	Rețea Neuronală Recurentă Slab Supervizată
Domeniu	Învățare profundă	Învățare profundă
Familie	Machine learning	Machine learning
Anul apariției	2016–2018	2009–2016
Autorul original	Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)	Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016)
Tip	Weakly supervised sequence model	Supervised learning under noisy or incomplete labels
Sursa seminală	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.	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.
Denumiri alternative	WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM	WS-RNN, distantly supervised RNN, noise-tolerant RNN, weakly supervised sequence model
Înrudite	6	5
Rezumat	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.	A weakly supervised RNN trains a recurrent neural network on sequences whose labels come from imperfect sources — heuristic rules, distant supervision, crowdsourcing, or generative label models — rather than expensive expert annotation. This lets researchers exploit large unlabeled corpora for sequential tasks such as text classification, named entity recognition, or time-series prediction when fully annotated data is scarce or costly.