Usporedite metode

	Slabo nadzirana rekurentna neuronska mreža	Dugo kratkoročno pamćenje (LSTM)
Područje	Duboko učenje	Duboko učenje
Obitelj	Machine learning	Machine learning
Godina nastanka	2009–2016	1997
Tvorac	Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016)	Hochreiter, S. & Schmidhuber, J.
Vrsta	Supervised learning under noisy or incomplete labels	Recurrent neural network with gated memory cells
Temeljni izvor	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.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
Drugi nazivi	WS-RNN, distantly supervised RNN, noise-tolerant RNN, weakly supervised sequence model	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
Srodne	5	4
Sažetak	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.	Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.