방법 비교

	약지도 학습 LSTM	Long Short-Term Memory (LSTM)
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2016–2018	1997
창시자	Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)	Hochreiter, S. & Schmidhuber, J.
유형	Weakly supervised sequence model	Recurrent neural network with gated memory cells
원전	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.
별칭	WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
관련	6	4
요약	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.	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.