Confronta i metodi

	LSTM a Supervisione Debole	Reti neurali ricorrenti
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2016–2018	1986–1990
Ideatore	Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)	Rumelhart, D. E.; Elman, J. L.
Tipo	Weakly supervised sequence model	Sequential neural network
Fonte seminale	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.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Alias	WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM	RNN, Elman network, Jordan network, simple recurrent network
Correlati	6	3
Sintesi	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 Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.