เปรียบเทียบวิธี

	Weakly Supervised LSTM	โครงข่ายประสาทเทียมแบบเวียนซ้ำ (Recurrent Neural Network - RNN)
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2016–2018	1986–1990
ผู้ริเริ่ม	Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)	Rumelhart, D. E.; Elman, J. L.
ประเภท	Weakly supervised sequence model	Sequential neural network
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM	RNN, Elman network, Jordan network, simple recurrent network
ที่เกี่ยวข้อง	6	3
สรุป	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.